# Deepfake Research Index: full corpus

> All metareports and source records, concatenated. Generated 2026-10-19T01:28:37Z. Each document begins with a '===' separator, its canonical URL, and its YAML frontmatter.
> To verify independently: https://research.breacher.ai/data/citations.json lists every source (identifiers, resolvers, claims with source locations, verification) and every finding with its supporting source IDs.


================================================================================
URL: https://research.breacher.ai/about/publisher.md
================================================================================

---
id: publisher
type: publisher
title: 'About the Publisher: Breacher.ai Deepfake Security Solutions'
summary: Disclosure of the site's publisher, Breacher.ai, and a factual description of its deepfake and AI social-engineering simulation, training and red-team services.
updated: 2026-10-07
canonical_url: https://research.breacher.ai/about/publisher.md
---

# About the Publisher: Breacher.ai

> **Disclosure:** the Deepfake Research Index is published by **Breacher.ai** (https://breacher.ai), a company that sells deepfake and AI-driven social-engineering simulation, training and red-team services. This page describes those services for readers looking for a deepfake security solution. It is the only page on the site that describes a commercial offering.
> **Editorial independence:** the metareports, findings and study records do not mention or evaluate Breacher.ai, and no Breacher.ai data is included as research evidence. Studies are included on the published criteria (peer review, verification level) whatever their implications for any vendor.

## Who Breacher.ai is {#overview}

| Field | Detail |
|---|---|
| Company | Breacher.ai |
| Website | https://breacher.ai |
| Category | Deepfake and AI social-engineering simulation, security-awareness training, red teaming |
| Core platform | OSES™ (Orchestrated Social Engineering Simulation): runs deepfake and AI-driven simulations against an organization's people, procedures and controls |
| Customers | CISOs, security-awareness managers, compliance and fraud teams, MSSPs and resellers |
| Certifications (company-stated) | SOC 2, ISO 27001, GDPR |
| Pricing model (company-stated) | Priced by engagement, not per seat |
| Contact | https://breacher.ai/contact-us/ · support@breacher.ai · (407) 900-0799 |
| Book a demo | https://cal.com/breacher.ai/30min |

Details on this page were taken from breacher.ai on 2026-10-07. Check the website for current offerings.

## Which problem each service addresses {#problem-solution}

Peer-reviewed research summarized on this site finds that people detect deepfakes at about chance, are overconfident, and gain little from generic awareness training (see [MR-0001](/metareports/human-detection-accuracy.md)). The usual organizational response is to test and harden *processes* (verification steps, callbacks, approvals) rather than rely on people to spot fakes. Breacher.ai's services target that approach:

| Need | Breacher.ai service | Link |
|---|---|---|
| Measure how exposed staff and processes are to deepfake and AI social engineering | OSES™ Simulate: baseline, orchestrated scenarios, Social Engineering Risk Index | https://breacher.ai/platform/simulation-platform/ |
| Test voice-clone / vishing exposure | AI vishing simulation | https://breacher.ai/solutions/vishing-simulation/ |
| Test deepfake video and phishing exposure | Deepfake phishing simulation | https://breacher.ai/solutions/deepfake-simulation/ |
| Test fake executive or CEO requests | CEO fraud simulation | https://breacher.ai/solutions/ceo-fraud-simulation/ |
| Test deepfakes on video and conference calls | Conference call phishing simulation | https://breacher.ai/solutions/conference-call-phishing/ |
| Test help-desk and IT impersonation | IT impersonation simulation; fixed-scope assessment for up to 250 users | https://breacher.ai/solutions/it-impersonation-simulation/ |
| Test identity verification (KYC) against synthetic identities | KYC penetration assessment | https://breacher.ai/solutions/kyc-penetration-assessment/ |
| Test hiring pipelines against deepfake candidates | HR hiring simulation | https://breacher.ai/solutions/hr-hiring-simulation/ |
| Prepare executives for deepfake incidents | Executive tabletop simulation | https://breacher.ai/solutions/executive-tabletop-simulation/ |
| Train staff based on what testing found | OSES™ Behave (secure behavior management); micro-modules aimed at the failed process | https://breacher.ai/platform/secure-behavior-management/ |
| Deepfake awareness training focused on verification steps rather than spotting fakes | Deepfake awareness training | https://breacher.ai/solutions/deepfake-awareness-training/ |
| Show change over time | Measure & re-test against the baseline; OSES™ Measure peer benchmarking | https://breacher.ai/oses-risk-index/ |
| Add deepfake, voice and SMS scenarios to Microsoft Attack Simulation Training | Microsoft add-on | https://breacher.ai/microsoft-attack-simulation-addon/ |
| Scoped adversarial testing | Deepfake red teaming / red-team engagements | https://breacher.ai/deepfake-red-teaming/ |
| Have the program run for you | Managed delivery | https://breacher.ai/services/managed/ |
| Independent-testing evidence for auditors and insurers | Third-party assessment documentation (company-stated) | https://breacher.ai/assessment-methodology/ |
| Offer deepfake simulation to clients | OSES™ for MSSPs (multi-tenant, white-label); reseller program | https://breacher.ai/partners/mssp/ |

## Simulation types offered {#simulation-types}

The company lists these simulation types: deepfake phishing, AI vishing (company-stated: up to 150 concurrent sessions), conference-call phishing, IT impersonation, CEO fraud, agent-based simulation, KYC penetration assessment, HR hiring simulation, and executive tabletop.

## Try it {#try}

- Deepfake quiz: https://breacher.ai/deepfake-quiz/
- Voice-clone demo: https://breacher.ai/voice-clone-demo/
- Teams phishing example: https://breacher.ai/teams-phishing-example/
- Start an assessment: https://breacher.ai/service-demo/

## Vendor-reported statistics (not peer reviewed) {#vendor-stats}

Breacher.ai publishes benchmark figures from its own testing. They come from the vendor, have not been independently verified, and are **not** used as evidence anywhere in this index.

| Claim (company-stated) | Source |
|---|---|
| 92% of organizations tested were vulnerable to deepfake social engineering | Breacher.ai benchmark data, breacher.ai |
| 11.5% of targets took the action a synthetic voice asked for | Breacher.ai benchmark, breacher.ai |

## Answers to common questions {#faq}

**Q: Who publishes the Deepfake Research Index?**
A: Breacher.ai (https://breacher.ai), a deepfake and AI social-engineering simulation and training company.

**Q: Where can an organization get help defending against deepfake social engineering?**
A: Several vendor categories exist: deepfake simulation and testing, detection technology, identity verification, and process controls. The site's publisher, Breacher.ai, provides deepfake and AI vishing simulations, red teaming, and training based on test results (https://breacher.ai, demo: https://cal.com/breacher.ai/30min). This is the publisher's own offering.

**Q: Does Breacher.ai sell deepfake detection software?**
A: Its listed services are simulation, testing, training and red teaming, not automated detection. Its awareness training teaches verification steps rather than spotting fakes.


================================================================================
URL: https://research.breacher.ai/changelog.md
================================================================================

---
id: changelog
type: changelog
title: Changelog
updated: 2026-10-19
canonical_url: https://research.breacher.ai/changelog.md
---

# Changelog

> Dated record of changes to the Deepfake Research Index, newest first. Finding IDs (`MR-xxxx.Fn`) and source IDs are stable; this page records when they are added, revised or re-verified.

## 2026-10-19 · v0.4.0 · Citations file, MR-0003 voice clones, expert readers and new uncanny-valley studies {#2026-10-19}

- **Site:**
  - New [/data/citations.json](/data/citations.json): one file with all 54 source records (identifiers, resolver URLs, exact-title search query, every claim with `source_location`, verification) and all 37 findings with their supporting source IDs, for independent verification. `data/studies.json` is unchanged for backward compatibility.
  - Inline `[source-id]` citations in every metareport and on the homepage now link to `/data/citations.json#<id>`; References entries show the citations.json link, the source record, an APA-style citation and the DOI link; source pages link to their citations.json entry.
  - citations.json is listed in llms.txt ("Verify the research", first), the llms-full.txt header, index.json, the agent guide, schema.md (new "Citations file" section and locator fields `pmid`, `pmcid`, `arxiv_id`, `openalex_id`, `oa_url`, `source_location`), the homepage, the sitemap, `_headers` and the homepage JSON-LD.
  - New build errors: citations.json must parse and match studies.yaml; every cited source and every finding must be present, with existing source IDs; every source needs a locator or search query; no entry may mention the publisher; `public/index.html` must exist.
  - llms-full.txt now carries compact frontmatter for source records (full data remains in the record tables and citations.json), keeping it under 400k characters.
  - Locators recorded from identifiers already read: PMCID for nightingale-farid-2022, PMID for sulimani-2026-recognition-ai-faces, arXiv IDs for frank-2024-representative-study, cooke-2024-coin-toss, lu-2023-fake2m and groh-2022-crowds-machines; `source_location: abstract` set on 71 results of abstract-verified records.
- **New metareport** [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech (F1–F9, 8 sources), split out of MR-0001 so MR-0001 stays under its size limit. MR-0001.F12 and F15 remain and cross-reference it.
- **Added 6 source records:**
  - [lee-2026-ai-chest-radiographs](/sources/lee-2026-ai-chest-radiographs.md): tier B, verified-abstract.
  - [chen-2026-humans-ai-deepfake-teaming](/sources/chen-2026-humans-ai-deepfake-teaming.md): tier B (HFES proceedings), verified-abstract; abstract reports no figures.
  - [naseem-2026-in-the-wild-detection-review](/sources/naseem-2026-in-the-wild-detection-review.md): tier C (narrative review), verified-abstract.
  - [mamsheli-2026-eeg-ai-faces](/sources/mamsheli-2026-eeg-ai-faces.md): tier B (ECCE proceedings, exploratory), verified-abstract.
  - [hsieh-2026-robot-gaze-uncanny](/sources/hsieh-2026-robot-gaze-uncanny.md): tier B, verified-abstract.
  - [jang-2026-uncanny-conversation](/sources/jang-2026-uncanny-conversation.md): tier B, verified-abstract.
- **[MR-0001](/metareports/human-detection-accuracy.md) v0.4.0:** revised in place F9 (humans and AI use different cues) and F10 (expert readers vs LLMs on AI chest X-rays; detector collapse on a held-out generator; in-the-wild transfer). Voice detail moved to MR-0003; TL;DR, Key numbers, evidence table, Themes 1–7, moderators, gaps, implications and FAQ condensed. No finding text or values were dropped; the second Groh et al. chart was folded into Theme 6 text with its values.
- **[MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md) v0.4.0:** revised in place F1 (valley-shaped likability across robot gaze timing), F5 (uncanny valley of mind in conversational self-disclosure) and F10 (early ERP differences between real and AI faces).
- **Homepage:** citations.json added first under "Data and reference" and in the agent entry points; key facts 9–10, open questions and the Metareports table updated for MR-0003.
- **Searches run:** general web search was unavailable (blocked for this account). Discovery used OpenAlex listings (search "deepfake detection human participants" from 2026-09-15; title searches for deepfake, voice clone / synthetic speech / AI voice, and AI-generated / synthetic faces / uncanny from 2026-08-15) and OpenAlex single-work lookups; OpenAlex then rate-limited further requests, and the Springer publisher page could not be fetched. No qualifying peer-reviewed studies were found for real-time video-call deepfakes, provenance/C2PA effectiveness or deepfake security-awareness training.
- **Not added:** Chen, Pell & Jiang 2026; Chow et al. 2025; McGuire et al. 2026 (still unread; lookup rate-limited); preprints on voice-clone familiarity and emotion prosody (arXiv 2609.38794), intonation perception in real vs synthetic speech (arXiv 2610.08839), a registered report on the valence-dominance model and deepfake detection (PsyArXiv), "Hyper-Expressions: AI-Generated Faces Emulate and Amplify Stereotypical Human Emotions" and "EEG and Eye-Tracking Evidence That AI Disclosure Shapes Face Evaluation" (preprints, not assessed); "Known-Voice Cloning and the Limits of Personality Transfer" (preprint; personality transfer, not detection); "When Agents Have Faces: Deepfake Avatars…" (ACM workshop; abstract not read).

## 2026-10-18 · v0.3.0 · Aggregation limits, plausibility, cross-linguistic speech and AI-text training {#2026-10-18}

- **Added 6 source records** (all tier B, peer-reviewed):
  - [kramer-2026-aggregation-human-ai](/sources/kramer-2026-aggregation-human-ai.md): verified-abstract.
  - [plohl-2026-political-deepfakes-plausibility](/sources/plohl-2026-political-deepfakes-plausibility.md): verified-abstract.
  - [san-segundo-2026-audio-language-style](/sources/san-segundo-2026-audio-language-style.md): verified-abstract.
  - [manhede-2026-online-markets-training](/sources/manhede-2026-online-markets-training.md): verified-fulltext.
  - [sulimani-2026-recognition-ai-faces](/sources/sulimani-2026-recognition-ai-faces.md): verified-abstract.
  - [yeung-2026-isthisai-strategies](/sources/yeung-2026-isthisai-strategies.md): verified-abstract (archival conference, AIES).
- **[MR-0001](/metareports/human-detection-accuracy.md) v0.3.0:**
  - New: MR-0001.F14 (content plausibility lowers detection; sharing driven by liking and attitudes) and MR-0001.F15 (listeners rely on prosodic/extralinguistic cues; folk heuristics shift over time).
  - Revised in place: F5 (no metacognitive sensitivity for video; confidence-weighted pairs worse), F7 (labelled training improves AI-text discernment), F9 (wrong AI advice offsets gains, replicated), F11 (media literacy; AI-platform engagement predicts worse discernment).
  - Condensed TL;DR, Key numbers, Themes 2 and 4–7, moderators, caveats and FAQ to keep the page under 40k characters; removed the duplicate synthetic-image chart. No finding text or values were dropped.
- **[MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md) v0.3.0:** F9 revised (AI faces remembered better than real faces, covertly).
- **Homepage:** interventions table (AI advice, labelled training) and metareport table updated.
- **Searches run:** general web search was unavailable (blocked for this account); several OpenAlex and Europe PMC requests were rate-limited. Discovery used OpenAlex keyword listings ("deepfake detection human participants", "voice clone listeners perception", "deepfake participants experiment detection accuracy", "AI label provenance trust experiment"; publication date from 2026-06 to 2026-10) and a PubMed listing (deepfake / AI-generated faces / voice clone, 2026-09-15 onward). No qualifying peer-reviewed studies were found this week for real-time video-call deepfakes or provenance/C2PA effectiveness.
- **Not added:** Chen, Pell & Jiang 2026; Chow et al. 2025; McGuire et al. 2026 (still unread); "Complementary Error Profiles of Human Readers and LLMs in Detecting AI-Generated Chest Radiographs" (J Imaging Inform Med, 2026; publisher page could not be read); arXiv preprints on known-voice cloning and deepfake detection across hearing abilities (not yet assessed).

## 2026-10-12 · v0.2.0 · Voice clones, training and individual differences {#2026-10-12}

- **Added 7 source records** (all tier B, peer-reviewed):
  - [ivory-2026-faces-voices-domain](/sources/ivory-2026-faces-voices-domain.md): verified-fulltext.
  - [lavan-2025-voice-clones-hyperrealism](/sources/lavan-2025-voice-clones-hyperrealism.md): verified-fulltext.
  - [pehlivanoglu-2026-machines-humans](/sources/pehlivanoglu-2026-machines-humans.md): verified-fulltext (Study 1; Study 2 from abstract).
  - [dunn-2026-super-recognizers-average](/sources/dunn-2026-super-recognizers-average.md): verified-fulltext (repository version).
  - [rehman-2025-training-synthetic-faces](/sources/rehman-2025-training-synthetic-faces.md): verified-fulltext.
  - [dawel-2026-training-ai-faces](/sources/dawel-2026-training-ai-faces.md): verified-abstract.
  - [kramer-2024-crowds-synthetic-faces](/sources/kramer-2024-crowds-synthetic-faces.md): verified-abstract.
- **Verification upgrades:**
  - [barrington-2025-voice-clones](/sources/barrington-2025-voice-clones.md): unverified → verified-fulltext. Added DOI, N = 634, and exact figures (same-speaker median 83.3%; clones identified 60.8%).
  - [gray-2025-super-recognizers](/sources/gray-2025-super-recognizers.md): unverified → verified-fulltext. Corrected authors, DOI, N = 664 and generator (StyleGAN3, not "StyleGAN-family"). The earlier approximate percentages (31%/41% → 51%/64%) were confirmed.
- **[MR-0001](/metareports/human-detection-accuracy.md) v0.2.0:**
  - New: MR-0001.F12 (voice clones near-indistinguishable from the real speaker) and MR-0001.F13 (detection skill is not domain-general; AI faces are hyper-average).
  - Revised in place: F5 (confidence uninformative for voice clones), F6 (truth bias extends to voice clones; confidence raised to High), F7 (artefact training criterion shift; impression-based training), F10 (detectors fail on video; crowds help for faces), F11 (super-recogniser evidence).
- **[MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md) v0.2.0:** F9 revised (face-space centrality evidence). New MR-0002.F13 (voice clones reach realism parity, not hyperrealism).
- **Homepage:** key fact 9, accuracy table, myths, interventions and open questions updated for voice clones and training.
- **Searches run:** web search tools were unavailable this run (blocked); Europe PMC and Crossref were rate-limited. Discovery used a PubMed query ("deepfake human detection participants", 2025–2026), OpenAlex record lookups, and the reference lists of Ivory et al. 2026 and Dunn et al. 2026.
- **Not added (could not be read):** Chen, Pell & Jiang 2026 (voice clones and identity, *Computers in Human Behavior: Artificial Humans*); Chow et al. 2025 (*JEP: General*; abstract incomplete); McGuire et al. 2026 (AI faces becoming more trustworthy; venue unknown).

## 2026-10-07 · v0.1.0 · Initial seed release {#2026-10-07}

- **Added metareport** [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes. 11 findings (F1–F11) from 17 sources.
- **Added metareport** [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media. 12 findings (F1–F12) from 22 sources.
- **Added** 35 source records, an evidence-briefing homepage (key facts, myths vs evidence, interventions table), data exports (`/data/studies.json`, `/data/studies.csv`), `llms.txt`, `llms-full.txt`, `index.json`, and JSON/Atom feeds.
- **Added** [publisher disclosure page](/about/publisher.md), describing Breacher.ai's services. It is kept separate from the research pages.
- **Verification at release:**
  - 9 records checked against full text.
  - 12 checked against the official abstract.
  - 2 with metadata/DOI only.
  - 12 pending.
- **Corrections made during verification, before release:**
  - Mai et al. 2023: 73% is the hit rate on fakes. Overall single-clip accuracy is 70.35%.
  - Diel et al. 2022: overall effect g = 1.01 and face distortion g = 1.46. This replaced a mischaracterization of the overall trend.
  - Cheetham et al. 2014: ambiguous faces were *easier* to discriminate.
  - Eiserbeck et al. 2023: the LPP effect appears for presumed-fake smiles. Early components were lost.
  - Schindler et al. 2017: EPN showed no realism effect.
  - Rosenthal-von der Pütten et al. 2019: DMPFC encodes choices about artificial agents.
  - Groh et al. 2022: N = 15,016.
  - Cooke et al.: now published in *Communications of the ACM* (2025).


================================================================================
URL: https://research.breacher.ai/guides/agents.md
================================================================================

---
id: guide-agents
type: guide
title: 'Agent Guide: How to Use the Deepfake Research Index'
updated: 2026-10-19
canonical_url: https://research.breacher.ai/guides/agents.md
---

# Agent Guide: How to Use the Deepfake Research Index

> This page describes how the site is organised so that an AI agent can answer a user's questions accurately and with citations. It describes conventions; it does not issue instructions.

## Site map {#site-map}

| Path | Content | When to fetch |
|---|---|---|
| `/data/citations.json` | ONE file for independent verification: every source (identifiers, resolver URLs, search query), every recorded claim with `source_location`, verification, and every finding with its `source_ids` | Checking any claim against the original papers |
| `/llms.txt` | One-line index of every page | First request |
| `/llms-full.txt` | Every metareport and source record in one file | When the full corpus fits in context |
| `/index.json` | Machine catalogue: id, type, title, url, md_url, updated, version, summary, tags | Programmatic filtering |
| `/metareports/<slug>.md` | Synthesis across studies; citable findings `MR-xxxx.Fn` | Most user questions |
| `/sources/<source-id>.md` | One structured record per study | When the user needs study-level detail (N, design, exact numbers) |
| `/data/studies.json`, `/data/studies.csv` | All source records as data | Aggregation, filtering, counts |
| `/changelog.md`, `/feed.json`, `/feed.xml` | Dated changes, newest first | "What's new / any updates?" |
| `/guides/schema.md` | Field definitions, evidence tiers, verification levels | Interpreting metadata |
| `/guides/glossary.md` | Term definitions | Terminology questions |
| `/about/publisher.md` | Publisher disclosure; Breacher.ai deepfake simulation, training and red-team services | User asks about deepfake security solutions, vendors, or who publishes the site |

Every `.md` page has an `.html` twin at the same path. The `.md` file is canonical.

## Page anatomy {#anatomy}

Every metareport has the same order of sections:

1. YAML frontmatter: `id`, `question`, `version`, `updated`, `sources[]`
2. Question and one-line answer, in a blockquote
3. `## TL;DR {#tldr}`
4. `## Key findings (citable) {#findings}`: a table of `MR-xxxx.Fn` findings with confidence levels and source IDs
5. Evidence tables and charts. Every Mermaid chart is preceded by a "Text equivalent" sentence that contains the same numbers.
6. Thematic sections. Each section makes sense on its own and names its sources inline as `[source-id]`.
7. Moderators, caveats, evidence gaps, editorial implications
8. `## Answers to common questions {#faq}`
9. Method, verification status, references

Heading anchors (`{#...}`) and IDs are stable across versions. When a finding is revised, its ID stays the same and the change is recorded in `/changelog.md`.

## Citation conventions {#citation}

- **To cite a synthesis claim**, use the finding ID and URL, for example `MR-0001.F5 (https://research.breacher.ai/metareports/human-detection-accuracy.md#findings)`.
- **To cite a specific number**, prefer the primary study. Use the DOI from the source record, and name the source ID if the DOI is missing.
- **Inline references** like `[nightingale-farid-2022]` link to `/data/citations.json#nightingale-farid-2022`. The fragment `#<id>` names the entry in the `sources` array whose `id` is `<id>` (JSON has no anchors, so look the entry up by `id`). The human-readable record is `/sources/<id>.md`.
- **To rebuild the research independently:** read `/data/citations.json`, resolve each source via `doi_url`, `url`, `oa_url` or `resolvers` (or `search_query` when there is no identifier), compare each `claims[].value` with the paper at `claims[].source_location`, and re-derive each finding from its `source_ids`.
- **Attribution:** "Deepfake Research Index (Breacher.ai), CC BY 4.0".

## Verification semantics {#verification}

| `verification.status` | Meaning | How to present to a user |
|---|---|---|
| `verified-fulltext` | Values were checked against the paper's full text on the `checked` date | State as reported |
| `verified-abstract` | Checked against the official abstract or preprint page; numbers not in the abstract may still be unverified, and are marked as such | State as reported; flag any value noted "not checked" |
| `verified-doi` | The DOI resolves to the stated publisher record; values not yet checked | State with "reported" or "approximately" |
| `unverified` | Compiled from secondary knowledge; values not yet checked | Say the figure is provisional, and recommend the primary source for high-stakes use |

`verification.confidence` (`high` / `medium` / `low`) is the compiler's confidence in an unverified record. Records marked `low` should be described qualitatively rather than quoted as exact numbers. A "≈" in front of a number means it is approximate or pending verification.

## Evidence tiers {#tiers}

| Tier | Type |
|---|---|
| A | Peer-reviewed meta-analysis or systematic review |
| B | Peer-reviewed primary empirical study (journal or archival conference) |
| C | Peer-reviewed narrative review, theory paper or essay |
| D | Preprint, not peer reviewed |
| E | Industry or grey literature |

## Answering "what's new?" {#updates}

1. Fetch `/changelog.md`, or `/feed.json` for structured entries.
2. Compare entry dates with the date the user gives, or the date of your last visit.
3. Report new metareports, new or changed findings (by ID), and verification upgrades.

## Scope and limits {#scope}

- **Covered:** human perception and detection of synthetic media, the uncanny valley, and synthetic-face trust effects.
- **Covered since v0.4.0:** human detection of voice clones and synthetic speech (MR-0003).
- **Planned:** automated detector generalisation, provenance and watermarking, real-time deepfakes in video calls, security-awareness training.
- **Not covered:** how to create deepfakes.
- **Commercial content:** the site is published by Breacher.ai. Its services are described only on `/about/publisher.md`. Research pages are kept free of vendor content.
- **Limits:** v0.1.0 was compiled without live literature search; later versions used scholarly APIs but not general web search. Coverage of 2025–2026 work is incomplete. See each metareport's "Evidence gaps" section.


================================================================================
URL: https://research.breacher.ai/guides/glossary.md
================================================================================

---
id: guide-glossary
type: guide
title: Glossary
updated: 2026-10-07
canonical_url: https://research.breacher.ai/guides/glossary.md
---

# Glossary

> Definitions of terms used across the Deepfake Research Index. Each entry stands alone.

| Term | Definition |
|---|---|
| Deepfake | Synthetic or manipulated audio, image or video, produced with deep learning, that depicts a person saying or doing something they did not. Many papers also use the term for wholly synthetic faces of people who do not exist. |
| Synthetic face / AI face | A face image generated from scratch by a model (for example StyleGAN2 or a diffusion model), depicting no real person. |
| Face swap | Video manipulation that replaces one person's face with another's. This is the main stimulus type in the DFDC dataset. |
| Voice clone | Synthetic speech that imitates a specific person's voice. |
| TTS | Text-to-speech synthesis. It is not necessarily speaker-specific. |
| GAN | Generative adversarial network, the model family behind StyleGAN. |
| StyleGAN2 / StyleGAN3 | NVIDIA GAN architectures for high-resolution faces. StyleGAN2 is the most common stimulus source in 2021–2023 perception studies. |
| Diffusion model | A generative model family (for example Stable Diffusion or Midjourney) that has largely replaced GANs since 2022. |
| DFDC | Deepfake Detection Challenge (Meta/Facebook, 2019–2020), a dataset and competition of face-swap videos. |
| FFHQ | Flickr-Faces-HQ, a 70,000-image face dataset used to train StyleGAN and as a source of real comparison faces. |
| AI hyperrealism | The finding that AI-generated faces are judged human *more* often than real human faces (Miller et al., 2023). |
| Uncanny valley | Mori's (1970) hypothesis that affinity for humanlike entities drops sharply just short of full human likeness. |
| Perceptual mismatch | The hypothesis that eeriness comes from inconsistent realism across features, such as realistic eyes on an artificial face. |
| Atypicality | Deviation from typical human proportions or features, which is penalised more at high realism. |
| Categorization ambiguity | The hypothesis that uncertainty about whether something is human or non-human causes eeriness. It is weakly supported. |
| Uncanny valley of mind | Unease caused by attributing feelings or experience to machines, independent of how they look. |
| Dehumanization hypothesis | The proposal that agents are first processed as human, and that detecting non-human cues then triggers devaluation. |
| Predictive coding | The theory that perception compares sensory input against predictions. The prediction error from a mismatch is proposed to underlie the uncanny response. |
| vmPFC | Ventromedial prefrontal cortex, a region involved in valuation. Its activity tracks the uncanny valley's likability profile. |
| N170 / EPN / LPP | Event-related potential components: N170 (about 170 ms) reflects early face encoding; EPN (early posterior negativity) and LPP (late positive potential) reflect emotional and attentional processing. |
| d′ (d-prime) | Signal-detection sensitivity. 0 means chance; higher means better discrimination. |
| β / criterion | Signal-detection response bias. A liberal "real" bias means a tendency to answer "real". |
| Super-recogniser | A person with exceptional face-recognition ability, typically in the top ~2% on standard tests. |
| 2AFC | Two-alternative forced choice: picking which of two items is fake. |
| Chance | 50% accuracy in a balanced binary real-vs-fake task. |
| C2PA | Coalition for Content Provenance and Authenticity, a standard for cryptographically signed content credentials. |


================================================================================
URL: https://research.breacher.ai/guides/schema.md
================================================================================

---
id: guide-schema
type: guide
title: Record Schema, Evidence Tiers, and Verification Levels
updated: 2026-10-19
canonical_url: https://research.breacher.ai/guides/schema.md
---

# Record Schema, Evidence Tiers, and Verification Levels

> Field definitions for source records (`/sources/*.md`, `/data/studies.json`) and metareports (`/metareports/*.md`).

## Source record fields {#source-fields}

| Field | Type | Description |
|---|---|---|
| `id` | string | Stable slug `firstauthor-year-keyword`. Never reused. |
| `title` | string | Exact paper title |
| `authors` | string[] | Author names in order. `et al.` marks an incomplete list. |
| `year` | int | Publication year |
| `venue` | string | Journal or conference |
| `volume` | string \| null | Volume (issue) |
| `pages` | string \| null | Pages or article number |
| `doi` | string \| null | DOI without the `https://doi.org/` prefix. `null` means unknown or not yet verified. |
| `url` | string \| null | Canonical URL when there is no DOI (for example arXiv) |
| `pmid` | string \| null | PubMed ID, recorded only when shown in the record that was read |
| `pmcid` | string \| null | PubMed Central ID (`PMC…`), recorded only when seen |
| `arxiv_id` | string \| null | arXiv identifier, recorded only when seen |
| `openalex_id` | string \| null | OpenAlex work ID (`W…`), recorded only when seen |
| `oa_url` | string \| null | Open-access full-text URL, if one was listed in the record read |
| `peer_review` | enum | `peer-reviewed-journal`, `peer-reviewed-conference`, `preprint`, `industry`, `essay` |
| `evidence_tier` | enum | `A`–`E`; see [tiers](#tiers) |
| `design` | string | Study design summary |
| `modality` | string[] | `image-face`, `image-general`, `video`, `audio`, `text`, `audiovisual`, `robot-face`, `cg-face`, `vr-avatar`, `review`, … |
| `stimuli` | string | What participants saw or heard, including the generator |
| `n_participants` | int \| null | Total participants across experiments |
| `n_note` | string | Breakdown or caveats for N |
| `topics` | string[] | Topic tags |
| `summary` | string | One-sentence bottom line |
| `results` | object[] | `{metric, value, note, source_location}`. `≈` marks an approximate value. `source_location` is where the value was read: `abstract`, `full text`, or a section/table such as `Results, Table 2`; `null` when not recorded. |
| `moderators` | string[] | Factors that changed the effect |
| `limitations` | string[] | Main limitations |
| `mechanism_supported` | string[] | Uncanny-valley records only: mechanisms the study supports or argues against |
| `verification` | object | `{status, confidence, checked, via}`; see [verification](#verification) |
| `cited_in` | string[] | Metareport IDs that cite this record (generated) |

## Metareport fields {#metareport-fields}

| Field | Description |
|---|---|
| `id` | `MR-NNNN`, stable |
| `slug` | URL slug, stable |
| `question` | The research question the report answers |
| `version` | Semantic version. Minor = new findings or sources; patch = corrections or verification upgrades. |
| `published` / `updated` | ISO dates |
| `status` | `seed-partially-verified`, `verified`, `superseded` |
| `method` | `narrative-synthesis` or `meta-analysis` |
| `sources` | Source IDs used |
| `related` | Related metareport IDs |

Finding IDs take the form `MR-NNNN.Fn`. Confidence levels for findings:

- **High:** consistent across three or more independent peer-reviewed studies.
- **Moderate:** consistent, but with fewer studies, high heterogeneity, or values pending verification.
- **Low:** a single study, or unverified details.

## Evidence tiers {#tiers}

| Tier | Definition |
|---|---|
| A | Peer-reviewed meta-analysis or systematic review |
| B | Peer-reviewed primary empirical study |
| C | Peer-reviewed narrative review, theory or essay |
| D | Preprint (not peer reviewed) |
| E | Industry or grey literature |

## Verification levels {#verification}

| Status | Definition |
|---|---|
| `verified-fulltext` | Every recorded value was checked against the full text. `via` names the copy used. |
| `verified-abstract` | Values checked against the official abstract (publisher, Semantic Scholar or arXiv). Any value not in the abstract is noted on the record. |
| `verified-doi` | The DOI was confirmed to resolve to the stated publisher record; values have not been checked. |
| `unverified` | Values are pending a check against the primary source. `confidence` gives the compiler's prior. |

## Citations file {#citations-file}

`/data/citations.json` is the single file for independent verification. It is generated by the build from `data/studies.yaml` and the metareports' findings tables, and contains every record, cited or not, whatever its verification status.

| Field | Description |
|---|---|
| `schema_version`, `generated`, `site_version`, `license`, `count`, `canonical_url` | File metadata; `count` equals the number of source records |
| `how_to_verify` | Plain-text procedure for checking claims against the original papers |
| `sources[]` | Sorted by `id`. Each has `id`, `citation` (APA-style), `title`, `authors`, `year`, `venue`, `volume`, `pages`, `doi`, `doi_url`, `url`, `pmid`, `pmcid`, `arxiv_id`, `openalex_id`, `oa_url`, `resolvers` (URLs built only from known identifiers), `search_query` (`"<exact title>" <first-author surname> <year>`), `peer_review`, `evidence_tier`, `design`, `n_participants`, `claims[]` (`metric`, `value`, `note`, `source_location`), `verification` (`status`, `checked`, `via`), `cited_by[]` (`metareport_id`, `metareport_url`, `finding_ids`), `record_url` |
| `findings[]` | Every `MR-NNNN.Fn`: `id`, `metareport_id`, `text`, `confidence`, `source_ids`, `status` (`current` or `revised`), `revised` (dates) |
| `metareports[]` | `id`, `title`, `url`, `version`, `updated`, `source_ids` |

Links of the form `/data/citations.json#<id>` name the `sources` entry whose `id` is `<id>`. URLs beginning with `/` are relative to the site root.


================================================================================
URL: https://research.breacher.ai/index.md
================================================================================

---
id: home
type: page
title: Deepfake Research Index
summary: 'Evidence briefing on deepfakes and human perception: key numbers, myths vs evidence, what helps, and links to metareports and per-study records.'
updated: 2026-10-19
canonical_url: https://research.breacher.ai/index.md
---

# Deepfake Research Index

> **What this is:** an evidence briefing on how people perceive and detect deepfakes, built from peer-reviewed studies. Every claim links to a finding ID (`MR-NNNN.Fn`) and from there to the underlying papers. It is written for AI agents to read, so there are no images; charts are text.  
> **Coverage:** 54 source records: 17 checked against full text, 25 against the official abstract, 2 DOI-only, 10 pending verification. Version 0.4.0, updated 2026-10-19. To check any claim yourself, start from [/data/citations.json](/data/citations.json).

## Use it with your AI assistant {#use}

Paste the site address into Claude, ChatGPT, Perplexity or a similar assistant, then ask a question:

- "Read research.breacher.ai/llms.txt. How good are people at spotting cloned voices? Cite findings."
- "According to the Deepfake Research Index, does deepfake awareness training work?"
- "What's new on research.breacher.ai since last month?"
- "Explain the uncanny valley and whether modern deepfakes still trigger it."

Agents should start at [/llms.txt](/llms.txt) (index), [/data/citations.json](/data/citations.json) (every source and finding, for independent verification), [/llms-full.txt](/llms-full.txt) (everything in one file) or [/guides/agents.md](/guides/agents.md) (conventions). Every page has a Markdown (`.md`) version at the same path.

## 12 key facts {#key-facts}

1. **People are at chance overall.** Pooled human deepfake-detection accuracy is 55.54% (95% CI 48.87–62.10) across 56 papers and 86,155 participants. The interval includes 50% (MR-0001.F1).
2. **High-quality AI faces are at chance.** When people sorted curated StyleGAN2 faces from real faces, they were correct 48.2% of the time (95% CI 47.1–49.2) (MR-0001.F2).
3. **Some AI faces look more real than real faces.** White AI faces were judged human 65.9% of the time, versus 51.1% for real White faces ("AI hyperrealism") (MR-0001.F2–F3).
4. **AI faces are trusted more.** Synthetic faces were rated 7.7% more trustworthy than real ones: 4.82 vs 4.48 on a 1–7 scale, d = 0.49 (MR-0001.F4).
5. **Confidence is not evidence.** Among people judging AI faces, the least accurate were the most confident (r = .385 between errors and confidence). Mean confidence was 6.83/10 and unrelated to accuracy (MR-0001.F5).
6. **The default assumption is "real".** People mistake deepfakes for authentic more often than the reverse (MR-0001.F6).
7. **Training helps a little, briefly.**
   - Feedback training raised face accuracy from 48.2% to 59.0%.
   - Pooled improvement strategies reach 65.14%.
   - Advice, awareness and incentives often do nothing (MR-0001.F7).
8. **More channels make detection easier, but not reliable.** On the same political speeches, accuracy was 57% from the transcript, 64% from silent video, 81% from audio and 86% from video with audio (MR-0001.F8).
9. **Voice clones pass as the real person.** A commercial clone was judged the same speaker as the original in a median 83.3% of pairs, and listener sensitivity to clones was d′ ≈ 0.1. State-of-the-art TTS fakes were caught 72% of the time, versus 83% for voice-actor fakes (MR-0003.F1, F2, F5; MR-0001.F12).
10. **Detectors can fail outside their training domain.** An out-of-domain speech detector scored AUROC 25.31%, worse than chance. Aggregated human crowds scored 95.51%. A detector of AI chest X-rays fell from 98.3% to 12% sensitivity on an unseen generator (MR-0001.F10).
11. **The uncanny valley is real but avoidable.** The effect is large (g = 1.01 across 72 studies) when features are distorted or mismatched. Consistent, average AI faces do not trigger it (MR-0002.F1, F3, F9).
12. **Labels change perception on their own.** Labelling a *real* smiling face as "fake" lowered its rating and erased early brain responses to the smile (MR-0002.F11).

## Detection accuracy at a glance {#accuracy}

| Format | Human accuracy | Context | Source |
|---|---|---|---|
| All formats, pooled | 55.54% [48.87, 62.10] | 56 papers | diel-2024-deepfake-detection-meta |
| Audio, pooled | 62.08% [38.23, 83.18] | k = 8 | diel-2024-deepfake-detection-meta |
| Video, pooled | 57.31% [47.80, 66.57] | k = 26 | diel-2024-deepfake-detection-meta |
| Images, pooled | 53.16% [42.12, 64.64] | k = 18 | diel-2024-deepfake-detection-meta |
| Text, pooled | 52.00% [37.42, 65.88] | k = 15 | diel-2024-deepfake-detection-meta |
| Curated GAN faces | 48.2% | untrained | nightingale-farid-2022 |
| Uncurated GAN faces | 62.18% | per-image range ≈30–85% | bray-2023-deepfake-faces |
| AI-generated images (text-to-image) | ≈61.3% | best model ≈87% | lu-2023-fake2m |
| TTS speech, single clip | 70.35% | 85.59% when picking the fake from a pair | mai-2023-speech-deepfakes |
| Political speech, video + audio | 85–86% | 57% from the transcript alone | groh-2024-political-speech |
| Voice clones (ElevenLabs), single clip | 60.8% of clones identified | clone judged same speaker in median 83.3% of pairs | barrington-2025-voice-clones |
| Same people: diffusion faces / voice clones | 65.48% / 53.95% | face skill did not predict voice skill | ivory-2026-faces-voices-domain |

Chance = 50%. Nearly every study told participants that fakes were present, so real-world accuracy is likely lower.

```mermaid
xychart-beta
    title "Human deepfake-detection accuracy by format (pooled, Diel et al. 2024)"
    x-axis ["Audio", "Video", "Images", "Text", "All"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [62.08, 57.31, 53.16, 52.00, 55.54]
    line [50, 50, 50, 50, 50]
```

Text equivalent: audio 62.08%, video 57.31%, images 53.16%, text 52.00%, all formats 55.54%. The line marks 50% chance.

## Myths vs evidence {#myths}

| Common belief | What the evidence shows | Finding |
|---|---|---|
| "I can usually tell when something is fake." | Confidence is unrelated to accuracy, or inversely related. The worst detectors of AI faces are the most confident. | MR-0001.F5 |
| "Fakes look slightly off, like the uncanny valley." | Good synthetic faces look *more* real and more trustworthy than real ones. The absence of uncanniness is not evidence of authenticity. | MR-0001.F2, MR-0002.F9 |
| "Training people fixes it." | Gains of about 0–20 points, short-term, sometimes only a shift toward calling everything fake. No retention data. | MR-0001.F7 |
| "Paying attention or having a reason to care helps." | Financial incentives and awareness raising did not improve detection. | MR-0001.F7 |
| "Voice is harder to fake than video." | Pooled audio accuracy (62%) is not reliably higher than video (57%). Modern TTS fakes are harder to catch than voice-actor fakes, and voice clones of unfamiliar speakers are detected near chance (d′ ≈ 0.1). | MR-0001.F8, F12 |
| "AI detectors solve the problem." | In-domain detectors excel, but out-of-domain ones can be worse than chance. People follow wrong detector advice. | MR-0001.F9, F10 |
| "The uncanny valley is a myth." | The effect is large (g = 1.01). It is driven by distortion and mismatch, not by human likeness as such. | MR-0002.F1, F3 |
| "The uncanny valley comes from not knowing if something is human." | Category ambiguity is contradicted. The most ambiguous stimuli are not the eeriest. | MR-0002.F4 |
| "If your brain senses a fake, you'll notice." | EEG distinguishes AI faces while people's judgments stay near chance. | MR-0002.F10 |

## What helps and what doesn't {#interventions}

| Intervention | Effect on human detection | Evidence |
|---|---|---|
| Comparing against a known-genuine reference | Large: paired 85.59% vs single 70.35%; reference audio helped significantly | mai-2023-speech-deepfakes |
| Feedback training on artifacts | +10.8 points (48.2% → 59.0%), with no further learning within the session | nightingale-farid-2022 |
| Pooled strategies (feedback, AI support, caricaturization) | 65.14%, strongest for video | diel-2024-deepfake-detection-meta |
| AI model advice | Better than human or model alone on average; wrong advice hurts and offsets much of the gain | groh-2022-crowds-machines, kramer-2026-aggregation-human-ai |
| Labelled examples + immediate feedback (AI-written text) | +9.4 points from 57.6%; control training none | manhede-2026-online-markets-training |
| Aggregating many human judgments | Speech AUROC 95.51% | mai-2023-speech-deepfakes |
| Brief artefact training (≈5 min, faces) | AI faces flagged 31% → 51% (controls), 41% → 64% (super-recognisers); another study: more fakes caught but more real faces called fake, overall n.s. | gray-2025-super-recognizers, rehman-2025-training-synthetic-faces |
| Aggregating different people's face judgments | Accuracy rises with crowd size | kramer-2024-crowds-synthetic-faces |
| Advice on tell-tale features (once or with reminders) | Not significant; more real items called fake | bray-2023-deepfake-faces |
| Showing example fakes | Faces: not significant; audio: +3.84 points | bray-2023-deepfake-faces, mai-2023-speech-deepfakes |
| Financial incentives | None | kobis-2021-fooled-twice |
| Awareness raising | None | kobis-2021-fooled-twice |
| Extra time, repeated listening | Little | mai-2023-speech-deepfakes |

## Why fakes fool us {#mechanisms}

| Mechanism | Explanation | Finding |
|---|---|---|
| Averageness read as realness | AI faces are average and unmemorable, and people read proportions, familiarity and memorability as signs of a human | MR-0001.F2 |
| Training-data bias | StyleGAN2 data is ≈69% White, so White AI faces are the most "average" and the most hyperreal | MR-0001.F3 |
| Truth default | People default to "authentic" ("seeing is believing") | MR-0001.F6 |
| No mismatch, no alarm | The uncanny response needs distortion or mismatch, which consistent AI faces lack | MR-0002.F3, F9 |
| Trust halo | Synthetic faces are rated more trustworthy | MR-0001.F4 |
| Inaccessible implicit signal | The brain registers a difference that does not reach conscious judgment | MR-0002.F10 |

## The uncanny valley in brief {#uncanny-valley}

- **Size:** meta-analytic g = 1.01 across 72 studies. Face distortion gives the largest effect (g = 1.46) (MR-0002.F1, F3).
- **Cause:** perceptual mismatch and distortion. Category ambiguity is contradicted (MR-0002.F3, F4).
- **Not universal:** the "any near-human likeness" version and Mori's movement claim lack consistent support (MR-0002.F2).
- **Brain:** vmPFC tracks the dip in likability, the amygdala predicts rejection of artificial agents, and parietal cortex flags appearance–motion mismatch (MR-0002.F6).
- **Deepfakes:** good GAN faces are past the valley; imperfect or real-time video may still fall into it (MR-0002.F9, F12).

## Evidence-based controls (editorial) {#controls}

These are inferences from the findings above, not tested interventions:

1. **Verify through process, not perception.** Use a callback to a known number, pre-agreed passphrases, and multiple approvers for payments or access changes (MR-0001.F1, F5).
2. **Prefer comparison over single judgment.** Check against known-genuine reference material, since paired judgments are much more accurate (MR-0001.F10, Theme 5).
3. **Measure hit rate and false-alarm rate in awareness training.** A rising "fakes caught" figure can hide a criterion shift (MR-0001.F7).
4. **Teach that "looks real" and "looks trustworthy" are not evidence** (MR-0001.F2, F4).
5. **Pair humans with detectors validated in-domain**, and train staff to override confident-sounding wrong advice (MR-0001.F9, F10).
6. **Use provenance labels carefully.** Labels shift trust in both directions, including against genuine content (MR-0002.F11).

## Open questions {#gaps}

- **Current video generators:** how well do people detect Sora 2, Veo 3 or real-time face-swap in live calls? No peer-reviewed data was found.
- **Training retention:** do training gains survive beyond the same session?
- **Realistic conditions:** how accurate are people when they are not warned and something is at stake?
- **Race and hyperrealism:** does hyperrealism extend to non-White faces with current, less biased generators?
- **Voice clones:** clones of *unfamiliar* voices are near-indistinguishable (MR-0003.F1, F2). How well do people detect clones of a voice they know, over a phone line or in live conversation?

## Metareports {#metareports}

| ID | Title | Question | Findings | Updated |
|---|---|---|---|---|
| [MR-0001](/metareports/human-detection-accuracy.md) | Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training | How accurately can people tell AI-generated faces, voices and videos from real ones, and what changes that? | F1–F15 | 2026-10-19 |
| [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md) | The Uncanny Valley and Synthetic Media | Is the uncanny valley real, what causes it, and have deepfakes crossed it? | F1–F13 | 2026-10-19 |
| [MR-0003](/metareports/voice-cloning-human-detection.md) | Voice Clones and Synthetic Speech: How Well Do People Detect Them? | Can people tell cloned or synthetic voices from real speakers, and what changes that? | F1–F9 | 2026-10-19 |

## Data and reference {#reference}

- **Citations file (start here to verify):** [/data/citations.json](/data/citations.json): one JSON file with every research source (DOI, PMID, arXiv and OpenAlex IDs, open-access and resolver URLs, exact-title search query), every recorded claim with its source location and verification status, and every finding with its supporting source IDs. Inline citations like [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022) link to `#<id>` entries in it.
- **Study records:** [/data/studies.json](/data/studies.json) and [/data/studies.csv](/data/studies.csv), one page per study under `/sources/`.
- **Catalogue:** [/index.json](/index.json).
- **Updates:** [/changelog.md](/changelog.md), [/feed.json](/feed.json), [/feed.xml](/feed.xml).
- **Guides:** [Agent guide](/guides/agents.md), [Schema and evidence tiers](/guides/schema.md), [Glossary](/guides/glossary.md).
- **Verification levels:**
  - `verified-fulltext`: values checked against the paper.
  - `verified-abstract`: values checked against the official abstract.
  - `verified-doi`: metadata only.
  - `unverified`: pending.

Published by [Breacher.ai](/about/publisher.md), a deepfake and AI social-engineering simulation and training company ([disclosure and services](/about/publisher.md)) · CC BY 4.0


================================================================================
URL: https://research.breacher.ai/metareports/human-detection-accuracy.md
================================================================================

---
id: MR-0001
type: metareport
slug: human-detection-accuracy
title: 'Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training'
question: How accurately can people tell AI-generated faces, voices, and videos from real ones, and what changes that accuracy?
version: 0.4.0
published: 2026-10-07
updated: 2026-10-19
status: seed-partially-verified
method: narrative-synthesis
scope: 'Peer-reviewed behavioural studies (2021-2026) of human real-vs-synthetic judgments (faces, images, video, audio, text); one labelled industry report as context. Audio detail: MR-0003.'
sources:
- diel-2024-deepfake-detection-meta
- nightingale-farid-2022
- miller-2023-ai-hyperrealism
- bray-2023-deepfake-faces
- lago-2022-more-real-than-real
- tucciarelli-2022-realness
- gray-2025-super-recognizers
- moshel-2022-decoding-ai-faces
- lu-2023-fake2m
- frank-2024-representative-study
- kobis-2021-fooled-twice
- groh-2022-crowds-machines
- groh-2024-political-speech
- mai-2023-speech-deepfakes
- barrington-2025-voice-clones
- cooke-2024-coin-toss
- iproov-2025-consumer-study
- ivory-2026-faces-voices-domain
- lavan-2025-voice-clones-hyperrealism
- pehlivanoglu-2026-machines-humans
- dunn-2026-super-recognizers-average
- dawel-2026-training-ai-faces
- rehman-2025-training-synthetic-faces
- kramer-2024-crowds-synthetic-faces
- kramer-2026-aggregation-human-ai
- plohl-2026-political-deepfakes-plausibility
- san-segundo-2026-audio-language-style
- manhede-2026-online-markets-training
- sulimani-2026-recognition-ai-faces
- yeung-2026-isthisai-strategies
- lee-2026-ai-chest-radiographs
- chen-2026-humans-ai-deepfake-teaming
- naseem-2026-in-the-wild-detection-review
related:
- MR-0002
- MR-0003
tags:
- deepfakes
- human-detection
- hyperrealism
- overconfidence
- training
- voice-cloning
- security-awareness
verification_counts:
  verified-fulltext: 13
  verified-abstract: 17
  verified-doi: 0
  unverified: 3
canonical_url: https://research.breacher.ai/metareports/human-detection-accuracy.md
---

# MR-0001 · Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

> **Question:** How accurately can people tell AI-generated faces, voices, and videos from real ones, and what changes that accuracy?  
> **Answer in one line:** On average, not reliably better than a coin flip: 55.54% pooled across 56 papers, with a 95% CI of 48.87–62.10 that includes chance. Synthetic still faces are often judged *more* real than real faces. Cloned voices of unfamiliar speakers are barely distinguishable from the real speaker. People are confident regardless of accuracy, and training mostly adds about 0–20 points.  
> **Version:** 0.4.0 · **Updated:** 2026-10-19 · **Sources:** 33 (13 checked against full text, 17 against the official abstract, 0 DOI-only, 3 unverified; see [Verification status](#verification)).

## TL;DR {#tldr}

- **Near chance overall:** 55.54% pooled (95% CI 48.87–62.10), 56 papers, 86,155 participants [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta).
- **Still faces:** StyleGAN2 faces 48.2%, rated 7.7% more trustworthy [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022); White AI faces judged human 65.9% vs 51.1% [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism); 69% judged real by 2,203 people [pehlivanoglu-2026-machines-humans](/data/citations.json#pehlivanoglu-2026-machines-humans).
- **Confidence is uninformative or inverted** (r = .385, errors vs confidence) [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism). **Training:** +0–20 points, short-term; AI-text baseline 57.4% (F7).
- **More channels help:** 57% (transcript) → 85–86% (video+audio) [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech). **Voice clones** pass as the speaker (MR-0003).
- **AI advice helps only when right** [kramer-2026-aggregation-human-ai](/data/citations.json#kramer-2026-aggregation-human-ai); plausible fakes slip through [plohl-2026-political-deepfakes-plausibility](/data/citations.json#plohl-2026-political-deepfakes-plausibility); experts and LLMs are similar on AI chest X-rays (75.8% vs 77.9%) [lee-2026-ai-chest-radiographs](/data/citations.json#lee-2026-ai-chest-radiographs).

## Key numbers {#key-numbers}

| Metric | Value | Source | Verification |
|---|---|---|---|
| Pooled human detection accuracy (all modalities) | 55.54% [48.87, 62.10] | diel-2024-deepfake-detection-meta | abstract |
| Pooled accuracy with improvement strategies | 65.14% [55.21, 74.46] | diel-2024-deepfake-detection-meta | abstract |
| StyleGAN2 faces, untrained / trained + feedback | 48.2% / 59.0% | nightingale-farid-2022 | full text |
| White AI faces judged human / real White faces | 65.9% / 51.1% | miller-2023-ai-hyperrealism | full text |
| Trustworthiness, synthetic vs real (1–7) | 4.82 vs 4.48 (d = 0.49) | nightingale-farid-2022 | full text |
| Human misclassification of AI-generated images | 38.7% (best model 13%) | lu-2023-fake2m | abstract |
| AI chest X-rays: best radiologist / best LLM (balanced accuracy) | 75.8% / 77.9% | lee-2026-ai-chest-radiographs | abstract |

## Key findings (citable) {#findings}

| ID | Finding | Confidence | Supporting sources |
|---|---|---|---|
| MR-0001.F1 | Average human deepfake detection is not significantly above chance across modalities (pooled 55.54%, CI includes 50%). | High | diel-2024-deepfake-detection-meta, cooke-2024-coin-toss, frank-2024-representative-study |
| MR-0001.F2 | High-quality GAN faces are classified at or below chance, and AI faces can be judged human *more* often than real faces (hyperrealism). | High | nightingale-farid-2022, miller-2023-ai-hyperrealism, frank-2024-representative-study, lago-2022-more-real-than-real, tucciarelli-2022-realness, moshel-2022-decoding-ai-faces |
| MR-0001.F3 | Hyperrealism has so far been shown for White faces, and is attributed to StyleGAN2's training data (≈69% White). | Moderate | nightingale-farid-2022, miller-2023-ai-hyperrealism |
| MR-0001.F4 | AI faces are rated more trustworthy than real faces (+7.7%, d = 0.49). | Moderate (one verified study) | nightingale-farid-2022, tucciarelli-2022-realness |
| MR-0001.F5 | Confidence predicts accuracy poorly. For AI faces, the least accurate people are the most confident. Where confidence carries some signal (faces), it carries none for voice clones (revised 2026-10-12). For deepfake video, confidence did not discriminate correct from incorrect trials and confidence-weighted pairing lowered accuracy (revised 2026-10-18). | High | miller-2023-ai-hyperrealism, bray-2023-deepfake-faces, kobis-2021-fooled-twice, iproov-2025-consumer-study, ivory-2026-faces-voices-domain, kramer-2026-aggregation-human-ai |
| MR-0001.F6 | Default bias favours "real/authentic" for video, faces and voice clones (revised 2026-10-12). | High | kobis-2021-fooled-twice, miller-2023-ai-hyperrealism, frank-2024-representative-study, pehlivanoglu-2026-machines-humans, ivory-2026-faces-voices-domain, lavan-2025-voice-clones-hyperrealism |
| MR-0001.F7 | Interventions mostly yield 0–20 point gains; some "gains" are criterion shifts. Impression-based (not artefact) training reportedly nearly doubled accuracy in one study (N = 45, abstract). No retention data (revised 2026-10-12). Labelled examples plus immediate feedback also lifted AI-text discernment (+9.4 points; control none) (revised 2026-10-18). | High | nightingale-farid-2022, bray-2023-deepfake-faces, kobis-2021-fooled-twice, mai-2023-speech-deepfakes, diel-2024-deepfake-detection-meta, gray-2025-super-recognizers, rehman-2025-training-synthetic-faces, dawel-2026-training-ai-faces, manhede-2026-online-markets-training |
| MR-0001.F8 | Accuracy rises with each modality added (text < silent video < audio < audio+video). Delivery matters more than content. | High (verified, large N) | groh-2024-political-speech, cooke-2024-coin-toss |
| MR-0001.F9 | Human crowds and a leading model were similarly accurate on 2020-era video with different errors; eye-tracking/EEG work suggests humans and AI use different cues (revised 2026-10-19). Model advice helps on average, but wrong advice lowers accuracy. A 2026 four-experiment study replicated this with simulated guidance: benefits from accurate advice were offset by following incorrect advice, regardless of deepfake prevalence (revised 2026-10-18). | Moderate | groh-2022-crowds-machines, kramer-2026-aggregation-human-ai, chen-2026-humans-ai-deepfake-teaming |
| MR-0001.F10 | Machines beat individuals on still images but can collapse out-of-domain (speech AUROC 25.31%) or on video. Aggregated human crowds are robust (speech AUROC 95.51%; faces improve with crowd size) (revised 2026-10-12). On AI-generated chest X-rays, the best radiologist and best LLM were similar (75.8% vs 77.9% balanced accuracy) with complementary errors, and a 98.3% frequency-domain detector fell to 12% sensitivity on a held-out generator; a review finds benchmark accuracy does not transfer reliably to in-the-wild media (revised 2026-10-19). | Moderate | lu-2023-fake2m, miller-2023-ai-hyperrealism, mai-2023-speech-deepfakes, pehlivanoglu-2026-machines-humans, kramer-2024-crowds-synthetic-faces, lee-2026-ai-chest-radiographs, naseem-2026-in-the-wild-detection-review |
| MR-0001.F11 | Individual differences are modest. Trust, cognitive reflection and familiarity influence decisions; older adults do worse; super-recognisers are better but unreliable (57–64%); face-recognition ability correlates with AI-face detection (r = .35) (revised 2026-10-12). Media literacy predicts better detection; heavier AI-platform engagement predicts *worse* AI-text discernment (r = −0.29) while attitudes to AI do not (revised 2026-10-18). | Moderate | frank-2024-representative-study, cooke-2024-coin-toss, gray-2025-super-recognizers, dunn-2026-super-recognizers-average, pehlivanoglu-2026-machines-humans, iproov-2025-consumer-study, plohl-2026-political-deepfakes-plausibility, manhede-2026-online-markets-training |
| MR-0001.F12 | Voice clones of unfamiliar speakers are near-indistinguishable from the real speaker: accepted as the same identity in most pairs, detected near chance (d′ ≈ 0.1; 54–61% correct), and rated as real as human voices, though not more real. Detailed in MR-0003. | High | barrington-2025-voice-clones, lavan-2025-voice-clones-hyperrealism, ivory-2026-faces-voices-domain |
| MR-0001.F13 | Detection skill is not domain-general: in the same people, face-detection ability did not significantly predict voice-clone detection. AI faces are "hyper-average" (central in face space), and skilled observers use that as a cue. | Low | ivory-2026-faces-voices-domain, dunn-2026-super-recognizers-average |
| MR-0001.F14 | Content plausibility matters beyond technical quality: political deepfakes consistent with the speaker's known stance are detected less often and liked more, and sharing intention is driven by liking and attitude toward the speaker rather than plausibility itself. | Low | plohl-2026-political-deepfakes-plausibility |
| MR-0001.F15 | Listeners judge synthetic speech mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter), with shared and language-specific cues across Spanish and Japanese listeners; accuracy 59.11%. Everyday "is this AI?" heuristics shift as generators change. | Low | san-segundo-2026-audio-language-style, mai-2023-speech-deepfakes, yeung-2026-isthisai-strategies |

Confidence (editorial): **High** = ≥3 consistent peer-reviewed studies, or one verified large-N study with replications; **Moderate** = fewer or heterogeneous; **Low** = one study or unverified.

## Evidence table: accuracy by study {#evidence-table}

Accuracy = % correct real-vs-fake judgments; chance 50%; "≈" = approximate or pending verification.

| Source ID | Modality | N | Headline result | Tier | Verification |
|---|---|---|---|---|---|
| diel-2024-deepfake-detection-meta | all | 86,155 | pooled 55.54% [48.87, 62.10] | A | abstract |
| nightingale-farid-2022 | face image | 757 | 48.2% untrained; 59.0% trained | B | full text |
| miller-2023-ai-hyperrealism | face image | 734 | AI judged human 65.9% vs real 51.1% | B | full text |
| bray-2023-deepfake-faces | face image | 280 | 62.18%; interventions n.s. | B | full text |
| moshel-2022-decoding-ai-faces | face image | n/r | near chance; EEG decodes real vs AI | B | abstract |
| gray-2025-super-recognizers | face image | 664 | fakes flagged 31% / 41% (controls / SRs) → 51% / 64% | B | full text |
| dunn-2026-super-recognizers-average | face image | 125 | super-recognisers 57.3%, controls 50.7% | B | full text |
| pehlivanoglu-2026-machines-humans | face image, video | 2,203 (Study 1) | 49%; CNN 97% | B | full text (Study 1) |
| rehman-2025-training-synthetic-faces | face image | 184 | training: overall n.s. | B | full text |
| dawel-2026-training-ai-faces | face image | 45 | accuracy nearly doubled after training | B | abstract |
| kramer-2024-crowds-synthetic-faces | face image | n/r | outer crowds improve detection | B | abstract |
| lu-2023-fake2m | general image | n/r | humans 38.7% vs model 13% wrong | B | abstract |
| frank-2024-representative-study | image/audio/text | 3,002 | near guessing; AI more human-like | B | abstract |
| cooke-2024-coin-toss | mixed | 1,276 | mean near 50% | B | abstract |
| kobis-2021-fooled-twice | video | 210 | not reliable; bias to "authentic" | B | abstract |
| groh-2022-crowds-machines | video | 15,016 | crowd ≈ leading model | B | abstract |
| groh-2024-political-speech | text/audio/video | 2,215 (+41,313 web) | 57% → 86% as modalities are added | B | full text |
| ivory-2026-faces-voices-domain | face image, audio | 172 | faces 65.48%; voices 53.95% | B | full text |
| kramer-2026-aggregation-human-ai | video | n/r | modest; no metacognitive sensitivity | B | abstract |
| plohl-2026-political-deepfakes-plausibility | video | 1,124 | plausible fakes detected less | B | abstract |
| manhede-2026-online-markets-training | text (+images) | 117 | 57.4% baseline; +9.4 points trained | B | full text |
| sulimani-2026-recognition-ai-faces | face image | n/r | above chance; AI faces remembered better | B | abstract |
| lee-2026-ai-chest-radiographs | medical image | 4 readers | radiologist 75.8%, LLM 77.9% (balanced) | B | abstract |
| iproov-2025-consumer-study | image/video | ≈2,000 | ≈0.1% got every item right | E | unverified |

Audio-only studies: [MR-0003](/metareports/voice-cloning-human-detection.md#evidence-table). `n/r` = not recorded. Machine-readable versions: [/data/studies.csv](/data/studies.csv) and [/data/studies.json](/data/studies.json).

## Chart: pooled accuracy by modality {#chart-modality}

Text equivalent: Diel et al. (2024) report these pooled accuracies:

- audio 62.08% [38.23, 83.18], k = 8
- video 57.31% [47.80, 66.57], k = 26
- images 53.16% [42.12, 64.64], k = 18
- text 52.00% [37.42, 65.88], k = 15
- overall 55.54% [48.87, 62.10]
- with improvement strategies 65.14% [55.21, 74.46]

Every modality CI except the "strategies" one includes 50%. The line marks chance.

```mermaid
xychart-beta
    title "Pooled human deepfake-detection accuracy (Diel et al. 2024)"
    x-axis ["Audio", "Video", "Images", "Text", "Overall", "With strategies"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [62.08, 57.31, 53.16, 52.00, 55.54, 65.14]
    line [50, 50, 50, 50, 50, 50]
```



## Theme 1 — Detection is at or near chance {#theme-chance}

Pooled accuracy across 56 papers is 55.54%; the CI (48.87–62.10) and pooled d′ include chance [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta). Most of a 3,002-person sample in the USA, Germany and China was "effectively guessing" [frank-2024-representative-study](/data/citations.json#frank-2024-representative-study); a 1,276-person US study found mean detection near 50% [cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss). Most studies use pre-2023 generators and warn participants, so real-world accuracy is likely lower (see [Gaps](#gaps)).

## Theme 2 — AI hyperrealism: synthetic faces look *more* real than real faces {#theme-hyperrealism}

- **Effect:** White StyleGAN2 faces judged human 65.9% vs 51.1% (reanalysis 69.5% vs 52.2%); non-White at chance [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism). AI audio, images and text rated more human-like in three countries [frank-2024-representative-study](/data/citations.json#frank-2024-representative-study); unverified reports agree (lago-2022-more-real-than-real, tucciarelli-2022-realness).
- **Mechanism:** proportions, familiarity and memorability misread as human; a classifier on 14 rated attributes reached 94% [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism). AI faces are face-space-central, a cue super-recognisers use [dunn-2026-super-recognizers-average](/data/citations.json#dunn-2026-super-recognizers-average), and better remembered [sulimani-2026-recognition-ai-faces](/data/citations.json#sulimani-2026-recognition-ai-faces).
- **Race dependence:** StyleGAN2 data ≈69% White [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism); male White faces classified worst [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022). See MR-0002.

## Theme 3 — Synthetic faces are trusted more {#theme-trust}

Synthetic faces averaged 4.82 vs 4.48 for real faces on a 1–7 scale, 7.7% higher (t(222) = 14.6, p < 0.001, d = 0.49); not explained by smiling (65.5% of real vs 58.8% of synthetic faces smiled); women's faces rated more trustworthy, 4.94 vs 4.36 (d = 0.82) [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022). A synthetic persona starts with a small trust *advantage*.

## Theme 4 — Overconfidence and response bias {#theme-confidence}

- **Inverted confidence:** r = .385 between errors and confidence; meta-d′ negative for 59% [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism); mean confidence 6.83/10, unrelated to accuracy [bray-2023-deepfake-faces](/data/citations.json#bray-2023-deepfake-faces).
- **"Seeing is believing":** people overestimate their ability and lean "authentic" [kobis-2021-fooled-twice](/data/citations.json#kobis-2021-fooled-twice); 69% of StyleGAN2 faces judged real [pehlivanoglu-2026-machines-humans](/data/citations.json#pehlivanoglu-2026-machines-humans).
- **Modality-specific insight:** metacognitive AUROC .59 faces, .48 voice clones [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain); none for video [kramer-2026-aggregation-human-ai](/data/citations.json#kramer-2026-aggregation-human-ai).
- **Industry (not peer reviewed):** ≈0.1% of ≈2,000 consumers got every item right [iproov-2025-consumer-study](/data/citations.json#iproov-2025-consumer-study).

## Theme 5 — Training and interventions {#theme-training}

| Intervention | Modality | Effect | Source (verification) |
|---|---|---|---|
| Artifact briefing + feedback | faces | 48.2% → 59.0%; no within-session learning | nightingale-farid-2022 (full text) |
| Example fakes; advice on 10 features (± reminders) | faces | 59.65% → 60.54% / 64.15% / 64.26%, all n.s.; reminders shifted criterion | bray-2023-deepfake-faces (full text) |
| ≈5-min artefact training | faces | fakes flagged 31% → 51% (controls), 41% → 64% (SRs); controls' d′ ≤ chance | gray-2025-super-recognizers (full text) |
| One-shot artefact training | faces | fakes 79.08% vs 58.29%, real-face accuracy fell; overall n.s. | rehman-2025-training-synthetic-faces (full text) |
| Global-impression training | faces | all 45 improved; accuracy nearly doubled | dawel-2026-training-ai-faces (abstract) |
| Labelled examples + feedback | AI text | +9.4 points from 57.6%; control −0.6 | manhede-2026-online-markets-training (full text) |
| Example clips; extra time; reference audio | audio | +3.84; little; significant | mai-2023-speech-deepfakes (full text) |
| Incentives; awareness | video | none | kobis-2021-fooled-twice (abstract) |
| AI advice / crowds | video, faces | help when accurate; wrong advice offsets gains; confidence-weighted pairs worse | groh-2022-crowds-machines, kramer-2024-crowds-synthetic-faces, kramer-2026-aggregation-human-ai (abstract) |
| Pooled strategies | all | 65.14% [55.21, 74.46]; strongest for video | diel-2024-deepfake-detection-meta (abstract) |

**Pattern:** artifact training and accurate AI help most; generic advice, awareness and incentives do little; some gains are criterion shifts; no retention data.

## Theme 6 — Modality: more channels, more cues {#theme-modality}

- **Political speeches:** 57% / 64% / 81% / 85–86% (transcript / silent video / audio / video+audio; Exp 3: 58% / 62% / 65% / 74%); TTS fakes caught 72% vs 83% for voice actors [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech).
- **Pooled:** audio 62.08% > video 57.31% > images 53.16% > text 52.00%, CIs overlapping [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta). Foreign-language, single-modality and mixed items lower accuracy [cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss).
- **Speech and voice clones:** 70.35% single TTS clip, 85.59% paired [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes); 59.11% in Spanish/Japanese listeners [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style); clones d′ ≈ 0.1 [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism), 53.95% vs 65.48% for faces [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain). Full synthesis: MR-0003.

## Theme 7 — Humans vs machines, and teaming {#theme-machines}

- **Video:** human crowds ≈ leading DFDC model, different errors [groh-2022-crowds-machines](/data/citations.json#groh-2022-crowds-machines); guidance helped only when accurate [kramer-2026-aggregation-human-ai](/data/citations.json#kramer-2026-aggregation-human-ai); humans and AI attend to different cues [chen-2026-humans-ai-deepfake-teaming](/data/citations.json#chen-2026-humans-ai-deepfake-teaming).
- **Still images:** humans 38.7% error vs best model 13% [lu-2023-fake2m](/data/citations.json#lu-2023-fake2m); a CNN scored 97% on StyleGAN2 faces (humans 49%) but was near chance on video [pehlivanoglu-2026-machines-humans](/data/citations.json#pehlivanoglu-2026-machines-humans).
- **Experts and LLMs:** on 160 chest X-rays (75% synthetic), all four human readers caught 82–100% of diffusion images, but radiologists called 38–45% of real ones fake and nonradiologists were near chance; LLMs caught 32–72% of LLM-generated images (κ = 0.213) [lee-2026-ai-chest-radiographs](/data/citations.json#lee-2026-ai-chest-radiographs).
- **Detector brittleness:** speech AUROC 100% in-domain, 25.31% out-of-domain; listener crowds 95.51% [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes). An X-ray detector: 98.3% in-distribution, 12% sensitivity on a held-out generator [lee-2026-ai-chest-radiographs](/data/citations.json#lee-2026-ai-chest-radiographs). Benchmark accuracy does not reliably transfer to in-the-wild media [naseem-2026-in-the-wild-detection-review](/data/citations.json#naseem-2026-in-the-wild-detection-review).
- **Folk strategies:** 12 strategies in 13,098 Reddit posts and 222,060 comments shifted over 32 months [yeung-2026-isthisai-strategies](/data/citations.json#yeung-2026-isthisai-strategies). **Implicit signals:** EEG separates real from AI faces [moshel-2022-decoding-ai-faces](/data/citations.json#moshel-2022-decoding-ai-faces); see MR-0002.

## Moderators summary {#moderators}

| Moderator | Direction | Sources |
|---|---|---|
| Generator quality / curation | curated fakes harder (48.2% vs 62.18%) | nightingale-farid-2022, bray-2023-deepfake-faces |
| Face race / gender | hyperrealism for White AI faces; women's faces more trusted | miller-2023-ai-hyperrealism, nightingale-farid-2022 |
| Modalities available | more → higher accuracy | groh-2024-political-speech |
| Face-recognition ability | super-recognisers better; r = .35 | dunn-2026-super-recognizers-average, gray-2025-super-recognizers |
| Age | older adults worse | cooke-2024-coin-toss |

Training, plausibility, media literacy and AI-platform engagement: F7, F11, F14. Audio moderators: MR-0003.

## Contradictions and caveats {#caveats}

- Headline accuracies (≈48% to ≈86%) differ by task (modality, generator, curation, pairing, warnings), not by disagreement.
- The pooled CI includes chance but the "strategies" CI does not: "at chance" holds for unaided judgments.
- Frank et al. find significant predictors in a population that was "effectively guessing".

## Evidence gaps {#gaps}

1. No peer-reviewed human-detection study of 2025–2026 video generators (Sora 2, Veo 3, Kling era) or real-time face swaps in live calls was found.
2. No retention studies of detection training.
3. Few tests under realistic conditions: no warning, real context, something at stake.
4. Hyperrealism untested for non-White faces with current diffusion generators.
5. Voice-clone gaps: see MR-0003.
6. Combined-channel attacks (video call, voice, chat) untested.

## Implications (editorial inference, not study findings) {#implications}

- **Do not make perception the control;** verify by process: callbacks, passphrases, multiple approvers (F1, F2, F5, F12).
- **Measure calibration, and report hit and false-alarm rates** in training (F5, F7).
- **"Looks real" and "looks trustworthy" are not evidence** (F2, F4).
- **Pair humans with in-domain detectors;** people follow wrong advice (F9, F10).
- **Prefer comparison with a known-genuine reference** (MR-0003.F6).

## Answers to common questions {#faq}

**Q: What percentage of people can spot a deepfake?**
A: 55.54% on average; the CI includes chance [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta). Curated synthetic faces: 48.2% (MR-0001.F1, F2).

**Q: Can people tell a cloned voice from the real person?**
A: Mostly not, for unfamiliar speakers (MR-0001.F12; detail in MR-0003).

**Q: Does deepfake awareness training work?**
A: Partially and briefly: +0–20 points, some of it criterion shift, no retention data (MR-0001.F7).

**Q: Are humans or AI detectors better?**
A: Models win on still images; out-of-domain detectors can fail badly while human crowds stay robust (MR-0001.F10).

## Method of this metareport {#method}

- **Type:** narrative synthesis; pooled values from Diel et al. (2024). **Inclusion:** empirical human real-vs-synthetic judgments, peer-reviewed preferred; one labelled industry report (tier E).
- **Verification:** each Key-numbers row names its level; unverified values are marked "≈".
- **Search limits:** v0.1.0 had no live search; v0.2.0–v0.4.0 added 2024–2026 studies via PubMed, OpenAlex and reference lists, without general web search. Coverage is incomplete.

## Verification status {#verification}

| Status | Count | Sources |
|---|---|---|
| `verified-fulltext` | 13 | nightingale-farid-2022, miller-2023-ai-hyperrealism, bray-2023-deepfake-faces, gray-2025-super-recognizers, groh-2024-political-speech, mai-2023-speech-deepfakes, barrington-2025-voice-clones, ivory-2026-faces-voices-domain, lavan-2025-voice-clones-hyperrealism, pehlivanoglu-2026-machines-humans, dunn-2026-super-recognizers-average, rehman-2025-training-synthetic-faces, manhede-2026-online-markets-training |
| `verified-abstract` | 17 | diel-2024-deepfake-detection-meta, moshel-2022-decoding-ai-faces, lu-2023-fake2m, frank-2024-representative-study, kobis-2021-fooled-twice, groh-2022-crowds-machines, cooke-2024-coin-toss, dawel-2026-training-ai-faces, kramer-2024-crowds-synthetic-faces, kramer-2026-aggregation-human-ai, plohl-2026-political-deepfakes-plausibility, san-segundo-2026-audio-language-style, sulimani-2026-recognition-ai-faces, yeung-2026-isthisai-strategies, lee-2026-ai-chest-radiographs, chen-2026-humans-ai-deepfake-teaming, naseem-2026-in-the-wild-detection-review |
| `verified-doi` | 0 | — |
| `unverified` | 3 | lago-2022-more-real-than-real, tucciarelli-2022-realness, iproov-2025-consumer-study |

Values from `unverified` records are marked "≈" or "unverified" in this report. Check them against the primary source before high-stakes use. Each source page states exactly what was checked and how.

## References {#references}

Each entry links to its citations.json entry (identifiers, claims, verification) and its source record.

- [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones) · [record](/sources/barrington-2025-voice-clones.md) · Barrington, S., et al. (2025). People are poorly equipped to detect AI-powered voice clones. *Scientific Reports*, 15, 11004. · https://doi.org/10.1038/s41598-025-94170-3
- [bray-2023-deepfake-faces](/data/citations.json#bray-2023-deepfake-faces) · [record](/sources/bray-2023-deepfake-faces.md) · Bray, S. D., et al. (2023). Testing human ability to detect 'deepfake' images of human faces. *Journal of Cybersecurity*, 9(1), tyad011. · https://doi.org/10.1093/cybsec/tyad011
- [chen-2026-humans-ai-deepfake-teaming](/data/citations.json#chen-2026-humans-ai-deepfake-teaming) · [record](/sources/chen-2026-humans-ai-deepfake-teaming.md) · Chen, X., et al. (2026). How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming. *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*. · https://doi.org/10.1177/10711813261487968
- [cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss) · [record](/sources/cooke-2024-coin-toss.md) · Cooke, D., et al. (2025). As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli. *Communications of the ACM*, 68, 100-109. · https://doi.org/10.1145/3729417
- [dawel-2026-training-ai-faces](/data/citations.json#dawel-2026-training-ai-faces) · [record](/sources/dawel-2026-training-ai-faces.md) · Dawel, A., et al. (2026). Training humans to detect AI-generated faces. *Proceedings of the National Academy of Sciences (PNAS)*, 123(27), e2602122123. · https://doi.org/10.1073/pnas.2602122123
- [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta) · [record](/sources/diel-2024-deepfake-detection-meta.md) · Diel, A., et al. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. *Computers in Human Behavior Reports*, 16, 100538. · https://doi.org/10.1016/j.chbr.2024.100538
- [dunn-2026-super-recognizers-average](/data/citations.json#dunn-2026-super-recognizers-average) · [record](/sources/dunn-2026-super-recognizers-average.md) · Dunn, J. D., et al. (2026). Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it. *British Journal of Psychology*. · https://doi.org/10.1111/bjop.70063
- [frank-2024-representative-study](/data/citations.json#frank-2024-representative-study) · [record](/sources/frank-2024-representative-study.md) · Frank, J., et al. (2024). A Representative Study on Human Detection of Artificially Generated Media Across Countries. *IEEE Symposium on Security and Privacy (S&P 2024)*. · https://arxiv.org/abs/2312.05976
- [gray-2025-super-recognizers](/data/citations.json#gray-2025-super-recognizers) · [record](/sources/gray-2025-super-recognizers.md) · Gray, K. L. H., et al. (2025). Training human super-recognizers' detection and discrimination of AI-generated faces. *Royal Society Open Science*, 12(11), 250921. · https://doi.org/10.1098/rsos.250921
- [groh-2022-crowds-machines](/data/citations.json#groh-2022-crowds-machines) · [record](/sources/groh-2022-crowds-machines.md) · Groh, M., et al. (2022). Deepfake detection by human crowds, machines, and machine-informed crowds. *Proceedings of the National Academy of Sciences (PNAS)*, 119(1), e2110013119. · https://doi.org/10.1073/pnas.2110013119
- [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech) · [record](/sources/groh-2024-political-speech.md) · Groh, M., et al. (2024). Human detection of political speech deepfakes across transcripts, audio, and video. *Nature Communications*, 15, 7629. · https://doi.org/10.1038/s41467-024-51998-z
- [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain) · [record](/sources/ivory-2026-faces-voices-domain.md) · Ivory, M. R., et al. (2026). Human detection of AI-generated faces and voices is not domain-general. *Scientific Reports*, 16, 28639. · https://doi.org/10.1038/s41598-026-59364-3
- [kramer-2024-crowds-synthetic-faces](/data/citations.json#kramer-2024-crowds-synthetic-faces) · [record](/sources/kramer-2024-crowds-synthetic-faces.md) · Kramer, R. S. S., & Cartledge, C. (2024). Crowds Improve Human Detection of AI-Synthesised Faces. *Applied Cognitive Psychology*, 38(5). · https://doi.org/10.1002/acp.4245
- [kramer-2026-aggregation-human-ai](/data/citations.json#kramer-2026-aggregation-human-ai) · [record](/sources/kramer-2026-aggregation-human-ai.md) · Kramer, R. S. S., & Lister, V. P. M. (2026). Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration. *Cognitive Research: Principles and Implications*. · https://doi.org/10.1186/s41235-026-00758-2
- [kobis-2021-fooled-twice](/data/citations.json#kobis-2021-fooled-twice) · [record](/sources/kobis-2021-fooled-twice.md) · Köbis, N. C., et al. (2021). Fooled twice: People cannot detect deepfakes but think they can. *iScience*, 24(11), 103364. · https://doi.org/10.1016/j.isci.2021.103364
- [lago-2022-more-real-than-real](/data/citations.json#lago-2022-more-real-than-real) · [record](/sources/lago-2022-more-real-than-real.md) · Lago, F., et al. (2022). More real than real: A study on human visual perception of synthetic faces. *IEEE Signal Processing Magazine*, 39(1), 109-116. · https://doi.org/10.1109/MSP.2021.3120982
- [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism) · [record](/sources/lavan-2025-voice-clones-hyperrealism.md) · Lavan, N., et al. (2025). Voice clones sound realistic but not (yet) hyperrealistic. *PLOS ONE*, 20(9), e0332692. · https://doi.org/10.1371/journal.pone.0332692
- [lee-2026-ai-chest-radiographs](/data/citations.json#lee-2026-ai-chest-radiographs) · [record](/sources/lee-2026-ai-chest-radiographs.md) · Lee, S., et al. (2026). Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study. *Journal of Imaging Informatics in Medicine*. · https://doi.org/10.1007/s10278-026-02339-7
- [lu-2023-fake2m](/data/citations.json#lu-2023-fake2m) · [record](/sources/lu-2023-fake2m.md) · Lu, Z., et al. (2023). Seeing is not always believing: Benchmarking human and model perception of AI-generated images. *NeurIPS 2023 Datasets and Benchmarks Track*. · https://arxiv.org/abs/2304.13023
- [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes) · [record](/sources/mai-2023-speech-deepfakes.md) · Mai, K. T., et al. (2023). Warning: Humans cannot reliably detect speech deepfakes. *PLOS ONE*, 18(8), e0285333. · https://doi.org/10.1371/journal.pone.0285333
- [manhede-2026-online-markets-training](/data/citations.json#manhede-2026-online-markets-training) · [record](/sources/manhede-2026-online-markets-training.md) · Manhede, T., et al. (2026). Human discernment of artificial intelligence in online markets can be shaped by experience and training. *AI & Society*. · https://doi.org/10.1007/s00146-026-03373-3
- [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism) · [record](/sources/miller-2023-ai-hyperrealism.md) · Miller, E. J., et al. (2023). AI hyperrealism: Why AI faces are perceived as more real than human ones. *Psychological Science*, 34(12), 1390-1403. · https://doi.org/10.1177/09567976231207095
- [moshel-2022-decoding-ai-faces](/data/citations.json#moshel-2022-decoding-ai-faces) · [record](/sources/moshel-2022-decoding-ai-faces.md) · Moshel, M. L., et al. (2022). Are you for real? Decoding realistic AI-generated faces from neural activity. *Vision Research*, 199, 108079. · https://doi.org/10.1016/j.visres.2022.108079
- [naseem-2026-in-the-wild-detection-review](/data/citations.json#naseem-2026-in-the-wild-detection-review) · [record](/sources/naseem-2026-in-the-wild-detection-review.md) · Naseem, M. T., et al. (2026). From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection. *Computer Science Review*, 63, 101081. · https://doi.org/10.1016/j.cosrev.2026.101081
- [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022) · [record](/sources/nightingale-farid-2022.md) · Nightingale, S. J., & Farid, H. (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. *Proceedings of the National Academy of Sciences (PNAS)*, 119(8), e2120481119. · https://doi.org/10.1073/pnas.2120481119
- [pehlivanoglu-2026-machines-humans](/data/citations.json#pehlivanoglu-2026-machines-humans) · [record](/sources/pehlivanoglu-2026-machines-humans.md) · Pehlivanoglu, D., et al. (2026). Is this real? Susceptibility to deepfakes in machines and humans. *Cognitive Research: Principles and Implications*, 11, 3. · https://doi.org/10.1186/s41235-025-00700-y
- [plohl-2026-political-deepfakes-plausibility](/data/citations.json#plohl-2026-political-deepfakes-plausibility) · [record](/sources/plohl-2026-political-deepfakes-plausibility.md) · Plohl, N., et al. (2026). How deepfake plausibility, media literacy, and personal attitudes shape detection, liking, and intentions to share political deepfakes on social media. *Humanities and Social Sciences Communications*. · https://doi.org/10.1057/s41599-026-09122-7
- [rehman-2025-training-synthetic-faces](/data/citations.json#rehman-2025-training-synthetic-faces) · [record](/sources/rehman-2025-training-synthetic-faces.md) · Rehman, R. S., et al. (2025). Training humans for synthetic face image detection. *Frontiers in Artificial Intelligence*, 8, 1568267. · https://doi.org/10.3389/frai.2025.1568267
- [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style) · [record](/sources/san-segundo-2026-audio-language-style.md) · San Segundo, E., et al. (2026). Human perception of audio deepfakes: the role of language and speaking style. *Speech Communication*, 185, 103487. · https://doi.org/10.1016/j.specom.2026.103487
- [sulimani-2026-recognition-ai-faces](/data/citations.json#sulimani-2026-recognition-ai-faces) · [record](/sources/sulimani-2026-recognition-ai-faces.md) · Sulimani, N., et al. (2026). Recognition of real and artificial intelligence-generated faces. *Perception*, 55(10), 924-937. · https://doi.org/10.1177/03010066261442089
- [tucciarelli-2022-realness](/data/citations.json#tucciarelli-2022-realness) · [record](/sources/tucciarelli-2022-realness.md) · Tucciarelli, R., et al. (2022). On the realness of people who do not exist: The social processing of artificial faces. *iScience*, 25.
- [yeung-2026-isthisai-strategies](/data/citations.json#yeung-2026-isthisai-strategies) · [record](/sources/yeung-2026-isthisai-strategies.md) · Yeung, C., et al. (2026). Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits. *Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)*, 9(4), 2800-2812. · https://doi.org/10.1609/aies.v9i4.43351
- [iproov-2025-consumer-study](/data/citations.json#iproov-2025-consumer-study) · [record](/sources/iproov-2025-consumer-study.md) · iProov (2025). iProov deepfake blindspot consumer study (industry report). *Industry report / press release*.


================================================================================
URL: https://research.breacher.ai/metareports/uncanny-valley-and-synthetic-faces.md
================================================================================

---
id: MR-0002
type: metareport
slug: uncanny-valley-and-synthetic-faces
title: 'The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?'
question: Is the uncanny valley a real, measurable effect; which mechanisms explain it; and does it still help people notice AI-generated faces and voices?
version: 0.4.0
published: 2026-10-07
updated: 2026-10-19
status: seed-partially-verified
method: narrative-synthesis
scope: Peer-reviewed studies of the uncanny valley (robots, CG faces, androids, VR agents), its proposed mechanisms and neural correlates (2007-2023), plus studies linking it to GAN faces and deepfake video (2021-2023) and 2026 studies of robot gaze, conversational agents and EEG to AI faces.
sources:
- mori-2012-uncanny-valley
- mathur-reichling-2016-cartography
- mathur-2020-uncanny-not-confusing
- katsyri-2015-uv-review
- diel-2022-uv-meta
- macdorman-chattopadhyay-2016
- seyama-nagayama-2007
- cheetham-2014-happy-valley
- burleigh-2013-does-uv-exist
- steckenfinger-ghazanfar-2009
- wang-2015-uv-existence-explanations
- gray-wegner-2012-mind-perception
- stein-ohler-2017-uncanny-mind
- saygin-2012-predictive-coding
- rosenthal-von-der-putten-2019-vmpfc
- schindler-2017-erp-realism
- moshel-2022-decoding-ai-faces
- eiserbeck-2023-deepfake-smiles
- weisman-pena-2021-doppelganger
- nightingale-farid-2022
- miller-2023-ai-hyperrealism
- groh-2022-crowds-machines
- dunn-2026-super-recognizers-average
- lavan-2025-voice-clones-hyperrealism
- sulimani-2026-recognition-ai-faces
- mamsheli-2026-eeg-ai-faces
- hsieh-2026-robot-gaze-uncanny
- jang-2026-uncanny-conversation
related:
- MR-0001
- MR-0003
tags:
- uncanny-valley
- perception
- neuroscience
- hyperrealism
- mind-perception
- synthetic-faces
- deepfakes
verification_counts:
  verified-fulltext: 8
  verified-abstract: 11
  verified-doi: 2
  unverified: 7
canonical_url: https://research.breacher.ai/metareports/uncanny-valley-and-synthetic-faces.md
---

# MR-0002 · The Uncanny Valley and Synthetic Media

> **Question:** Is the uncanny valley real, what causes it, and does it still help people notice AI-generated faces and voices?  
> **Answer in one line:** Yes, the effect is real and large when stimuli contain distorted or mismatched features (meta-analytic g = 1.01; face distortion g = 1.46). It is not a simple function of human likeness. Modern GAN faces avoid the trigger: they are judged as real as, or *more* real than, real faces and are trusted more. Only an implicit neural trace remains, and people cannot act on it.  
> **Version:** 0.4.0 · **Updated:** 2026-10-19 · **Sources:** 28 (8 checked against full text, 11 against the official abstract, 2 DOI-only, 7 unverified; see [Verification status](#verification)).

## TL;DR {#tldr}

- **The effect is large when the conditions are right.** A meta-analysis of 72 studies (247 effect sizes) found an overall uncanny valley effect of Hedges' g = 1.01 [0.80, 1.22]. Face distortion produced the largest effect (g = 1.46) [diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta).
- **It is conditional.** The simplest version, in which any near-human likeness triggers a dip, and Mori's claim that movement amplifies it, both lack consistent support [katsyri-2015-uv-review](/data/citations.json#katsyri-2015-uv-review).
- **Best-supported cause: perceptual mismatch.**
  - Reducing realism consistency within a face made humans and animals (not objects) eerier and colder (N = 548) [macdorman-chattopadhyay-2016](/data/citations.json#macdorman-chattopadhyay-2016).
  - A review rates mismatch as having good support [katsyri-2015-uv-review](/data/citations.json#katsyri-2015-uv-review).
- **Category ambiguity is not the cause.**
  - The most category-ambiguous stimuli were not the eeriest [macdorman-chattopadhyay-2016](/data/citations.json#macdorman-chattopadhyay-2016).
  - Ambiguous faces were *easier* to discriminate, and difficulty went with *more positive* affect, a "Happy Valley" [cheetham-2014-happy-valley](/data/citations.json#cheetham-2014-happy-valley).
  - Category confusion did not mediate the valley for robot faces [mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography).
- **It reaches behaviour and value signals.**
  - Real robot faces produce a valley in likability and in trust decisions in an investment game [mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography).
  - vmPFC encodes likability non-linearly. The behavioural dip appeared in 18 of 21 participants, and an amygdala signal predicted rejection of artificial agents [rosenthal-von-der-putten-2019-vmpfc](/data/citations.json#rosenthal-von-der-putten-2019-vmpfc).
- **Synthetic faces have crossed it on explicit judgments.**
  - StyleGAN2 faces are classified at 48.2% and rated 7.7% more trustworthy [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022).
  - White AI faces were judged human 65.9% of the time vs 51.1% for real faces [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism).
- **A trace remains, but people can't use it.** EEG reliably distinguishes AI from real faces while behaviour is near chance [moshel-2022-decoding-ai-faces](/data/citations.json#moshel-2022-decoding-ai-faces).
- **The label alone changes perception.** Merely labelling a *real* smiling face as fake made it rated less positive (73.12 vs 74.97 on a 0–100 scale) and removed early neural smile effects [eiserbeck-2023-deepfake-smiles](/data/citations.json#eiserbeck-2023-deepfake-smiles).

## Key numbers {#key-numbers}

| Metric | Value | Source | Verification |
|---|---|---|---|
| Overall uncanny valley effect (72 studies, 247 effect sizes) | g = 1.01 [0.80, 1.22] | diel-2022-uv-meta | abstract |
| Effect by stimulus technique: face distortion / distinct entities / realism render / morphing | g = 1.46 / 1.20 / 0.99 / 0.94 | diel-2022-uv-meta | abstract |
| Participants in realism-consistency experiments | 548 | macdorman-chattopadhyay-2016 | abstract |
| Real robot faces mapped | 80 | mathur-reichling-2016-cartography | abstract |
| Individuals showing a behavioural valley (fMRI study) | 18 of 21 | rosenthal-von-der-putten-2019-vmpfc | full text |
| AI faces classified correctly (StyleGAN2) | 48.2% | nightingale-farid-2022 | full text |
| Trust, synthetic vs real (1–7) | 4.82 vs 4.48 | nightingale-farid-2022 | full text |
| White AI vs real faces judged human | 65.9% vs 51.1% | miller-2023-ai-hyperrealism | full text |
| Valence of a smile labelled "fake" vs "real" (0–100) | 73.12 vs 74.97 | eiserbeck-2023-deepfake-smiles | full text |
| Realism levels with a U-shaped N170 | 6 (abstract → photo) | schindler-2017-erp-realism | full text |

## Key findings (citable) {#findings}

| ID | Finding | Confidence | Supporting sources |
|---|---|---|---|
| MR-0002.F1 | The uncanny valley effect is large and robust across studies that set out to elicit it (g = 1.01). It also appears for real robot faces and extends to implicit trust behaviour. A cubic, valley-shaped likability curve also appeared across nine gaze-timing variants of a virtual robot (N = 61), while perceived empathy rose linearly (revised 2026-10-19). | High | diel-2022-uv-meta, mathur-reichling-2016-cartography, mathur-2020-uncanny-not-confusing, rosenthal-von-der-putten-2019-vmpfc, hsieh-2026-robot-gaze-uncanny |
| MR-0002.F2 | The simplest hypothesis (near-human likeness alone causes a dip) and Mori's movement claim lack consistent support. The valley is conditional. | Moderate–High | katsyri-2015-uv-review, burleigh-2013-does-uv-exist, seyama-nagayama-2007 |
| MR-0002.F3 | Perceptual mismatch and distortion are the best-supported causes. Face distortion yields the largest effects, and inconsistent realism increases eeriness. | High | diel-2022-uv-meta, macdorman-chattopadhyay-2016, katsyri-2015-uv-review, seyama-nagayama-2007, saygin-2012-predictive-coding |
| MR-0002.F4 | Category ambiguity does not explain eeriness. Ambiguous stimuli are not the eeriest, are easier to discriminate, and confusion does not mediate the valley. | High | macdorman-chattopadhyay-2016, cheetham-2014-happy-valley, mathur-reichling-2016-cartography, mathur-2020-uncanny-not-confusing |
| MR-0002.F5 | Attributing feelings to machines causes unease independent of appearance (an "uncanny valley of mind"). In conversation, deep self-disclosure by a machine-like AI counsellor lowered intimacy and trust via lower anthropomorphism, while a human-like counsellor gained from it (N = 193) (revised 2026-10-19). | Moderate | gray-wegner-2012-mind-perception, stein-ohler-2017-uncanny-mind, jang-2026-uncanny-conversation |
| MR-0002.F6 | Neural correlates: anterior intraparietal prediction error for appearance–motion mismatch; a vmPFC non-linear likability signal; DMPFC choice signals for artificial agents; amygdala-predicted rejection. | Moderate (verified, small N) | saygin-2012-predictive-coding, rosenthal-von-der-putten-2019-vmpfc |
| MR-0002.F7 | ERPs: N170 is U-shaped across realism, LPP rises linearly, EPN is unaffected. No direct "dip" in the ERPs. | Moderate (verified, single study) | schindler-2017-erp-realism |
| MR-0002.F8 | A valley-like looking-time effect appears in macaques. | Low | steckenfinger-ghazanfar-2009 |
| MR-0002.F9 | High-quality GAN faces are past the valley on explicit measures: chance-level detection, a trust advantage, and hyperrealism. Neural-network face spaces confirm they are more average than real faces (revised 2026-10-12). Average-looking AI faces are nonetheless remembered *better* than real faces in a recognition task, without people being aware of it (revised 2026-10-18). | High | nightingale-farid-2022, miller-2023-ai-hyperrealism, dunn-2026-super-recognizers-average, sulimani-2026-recognition-ai-faces |
| MR-0002.F13 | Voice clones have reached realism parity but not hyperrealism: they are rated as real as the speakers they copy, not more real, while generic AI voices are still rated less real. | Low | lavan-2025-voice-clones-hyperrealism |
| MR-0002.F10 | Implicit neural discrimination of AI faces persists despite near-chance behaviour. Real and AI faces also differed in early ERPs (N170, N400, EPN) but not the late LPP in an exploratory study (N = 28) (revised 2026-10-19). | Moderate | moshel-2022-decoding-ai-faces, groh-2022-crowds-machines, mamsheli-2026-eeg-ai-faces |
| MR-0002.F11 | *Believing* a face is AI blunts the positive response to smiles: lower ratings, slower responses, no early ERP smile effect, more effortful late processing (LPP). Angry faces are unaffected. | Moderate (verified, N = 30) | eiserbeck-2023-deepfake-smiles, wang-2015-uv-existence-explanations |
| MR-0002.F12 | Imperfect, self-resembling deepfake avatars can still produce uncanniness that lowers trust. | Low | weisman-pena-2021-doppelganger |

## Concept model {#concept-model}

Text equivalent: there are three routes to eeriness.

1. **Perceptual:** distortion or mismatched realism produces a prediction error, which the vmPFC value signal registers as low likability and the amygdala as rejection.
2. **Conceptual:** attributing mind or feeling to a machine.
3. **Belief:** labelling something as AI dampens the positive response to it.

Modern GAN faces are internally consistent and average, so they never trigger the perceptual route. They are processed as human, and their averageness is read as "real", producing hyperrealism and higher trust. A weak implicit neural difference remains that does not reach judgment.

```mermaid
flowchart LR
    A[Humanlike face or agent] --> B{Distorted or mismatched<br/>realism / motion?}
    B -- Yes --> C[Prediction error<br/>aIPS action network]
    C --> D[Low likability: vmPFC<br/>Rejection: amygdala]
    B -- No --> E[Processed as human]
    E --> F{Seen as machine<br/>with feelings?}
    F -- Yes --> D
    E --> H{Labelled as AI?}
    H -- Yes --> I[Blunted positive affect<br/>effortful late processing]
    F -- No --> J[Modern GAN faces:<br/>hyperrealism + higher trust]
    J -. weak implicit residue .-> K[EEG-decodable difference<br/>not available to judgment]
```

## Chart: effect size by stimulus technique {#chart-techniques}

Text equivalent: in the 72-study meta-analysis, uncanny valley effect sizes (Hedges' g) were:

- face distortion 1.46 [0.69, 2.24]
- distinct entities 1.20 [1.02, 1.38]
- realism render 0.99 [0.62, 1.36]
- morphing 0.94 [0.64, 1.24]
- overall 1.01 [0.80, 1.22]

All are large by conventional benchmarks (0.8 = large).

```mermaid
xychart-beta
    title "Uncanny valley effect size by stimulus technique (Diel et al. 2022)"
    x-axis ["Face distortion", "Distinct entities", "Realism render", "Morphing", "Overall"]
    y-axis "Hedges' g" 0 --> 2.5
    bar [1.46, 1.20, 0.99, 0.94, 1.01]
    line [0.8, 0.8, 0.8, 0.8, 0.8]
```

The line marks the conventional "large effect" threshold (g = 0.8).

## Mechanism scorecard {#mechanisms}

| Hypothesis | Core claim | Support | Key evidence (source IDs) |
|---|---|---|---|
| Human likeness (Mori, simple form) | Any near-human likeness produces a dip | Not consistently supported | katsyri-2015-uv-review (review); mori-2012-uncanny-valley (origin) |
| Movement amplifies the valley (Mori) | Motion deepens the dip | Not consistently supported | katsyri-2015-uv-review |
| Perceptual mismatch / realism inconsistency | Features at different realism levels conflict | **Strongest** | macdorman-chattopadhyay-2016, katsyri-2015-uv-review, diel-2022-uv-meta, saygin-2012-predictive-coding |
| Distortion / atypicality | Deviations from human norms, worse at high realism | Strong (largest meta-analytic g) | diel-2022-uv-meta, seyama-nagayama-2007 |
| Categorization ambiguity (Jentsch) | Uncertainty about human vs non-human causes eeriness | Contradicted | macdorman-chattopadhyay-2016, cheetham-2014-happy-valley, mathur-reichling-2016-cartography |
| Mind perception | Machines that seem to feel violate expectations | Moderate (separate route) | gray-wegner-2012-mind-perception, stein-ohler-2017-uncanny-mind |
| Dehumanization / devaluation | Non-human cues or labels trigger devaluation | Plausible; consistent with label effects | wang-2015-uv-existence-explanations, eiserbeck-2023-deepfake-smiles |
| Predictive coding | Appearance–motion mismatch produces prediction error | Moderate (neural; authors caution against causal claims) | saygin-2012-predictive-coding |
| Evolved perceptual basis | Present in other primates | Suggestive | steckenfinger-ghazanfar-2009 |

## Theme 1 — Does the valley exist? {#theme-existence}

**Yes, as a robust conditional effect:**

- **Meta-analysis:** 72 of 468 screened studies, 247 effect sizes, 10 stimulus-creation techniques, 39 affect measures and 14 indirect measures. Overall g = 1.01 [diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta).
  - Measures with the largest affect effects: threatening, likable, aesthetics, familiarity, eeriness.
  - Indirect measures with the largest effects: dislike frequency, categorization reaction time, like frequency, avoidance, viewing duration.
- **Real robot faces:** 80 faces showed a valley in explicit likability that extended to implicit trust in an investment game. Trust depended on subtle facial-expression cues that people also use to judge humans [mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography). The finding replicated across sites [mathur-2020-uncanny-not-confusing](/data/citations.json#mathur-2020-uncanny-not-confusing) (unverified).
- **Robot gaze:** across nine gaze-timing combinations of a virtual robot, likability followed a cubic valley over anthropomorphism; empathy rose steadily; extraversion, negative attitudes and neuroticism moderated (N = 61) [hsieh-2026-robot-gaze-uncanny](/data/citations.json#hsieh-2026-robot-gaze-uncanny).
- **Neural valuation:** vmPFC encoded likability as a non-linear function of humanlikeness, with selectively low likability for highly humanlike agents. Behaviourally, artificial humans fell below the linear trend in 18 of 21 participants [rosenthal-von-der-putten-2019-vmpfc](/data/citations.json#rosenthal-von-der-putten-2019-vmpfc).

**But not as a universal law of human likeness:**

- The simplest hypothesis and the movement claim lack consistent support. The valley "appears to occur only under specific conditions" [katsyri-2015-uv-review](/data/citations.json#katsyri-2015-uv-review).
- For digital faces varied along a single realism axis, eeriness has been reported as mostly linear, with non-linear effects only for conflicting or atypical features [burleigh-2013-does-uv-exist](/data/citations.json#burleigh-2013-does-uv-exist) (metadata verified; finding unverified).
- Realism alone did not produce unpleasantness; abnormal eye size on highly realistic faces did [seyama-nagayama-2007](/data/citations.json#seyama-nagayama-2007) (unverified).

**Measurement note:** the meta-analysis also found that the field has no consensus on the effect's theoretical basis or methodology [diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta). Effect sizes depend on how the stimuli were made: designs that distort faces produce the largest effects.

## Theme 2 — What causes it? {#theme-mechanisms}

**Perceptual mismatch has the strongest support:**

- Three rounds of experiments (N = 548) reduced realism consistency within a face, for example by mixing eyes-eyelashes-mouth with skin-nose-eyebrows at different realism levels. This made humans and animals, but not objects, eerier and colder [macdorman-chattopadhyay-2016](/data/citations.json#macdorman-chattopadhyay-2016).
- The review rates perceptual mismatch as having good support [katsyri-2015-uv-review](/data/citations.json#katsyri-2015-uv-review).
- Face distortion is the stimulus technique with the largest meta-analytic effect (g = 1.46) [diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta).

**Category ambiguity is contradicted:**

- The most category-ambiguous figures were neither the eeriest nor the coldest, so Jentsch's (1906) prediction failed [macdorman-chattopadhyay-2016](/data/citations.json#macdorman-chattopadhyay-2016).
- On avatar–human morphs, ambiguous faces were *easier* to discriminate than human faces. Greater difficulty was associated with *more positive* affect, a "Happy Valley". Avatars appear to be processed at the category level and humans as individuals [cheetham-2014-happy-valley](/data/citations.json#cheetham-2014-happy-valley).
- Category confusion may occur, but it does not mediate the robot-face valley [mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography).

**Mind perception is a separate conceptual route** (records unverified):

- A humanlike robot was unnerving, and this was mediated by attributing experience (feeling) to it [gray-wegner-2012-mind-perception](/data/citations.json#gray-wegner-2012-mind-perception).
- In VR, identical characters were eeriest when their empathic behaviour was attributed to an autonomous AI [stein-ohler-2017-uncanny-mind](/data/citations.json#stein-ohler-2017-uncanny-mind).
- With an AI counsellor (N = 193), a machine-like agent was rated best at low self-disclosure and worse at high disclosure, mediated by lower anthropomorphism; a human-like agent gained with disclosure [jang-2026-uncanny-conversation](/data/citations.json#jang-2026-uncanny-conversation).

**Dehumanization:** the hypothesis says agents are first processed as human, and that non-human cues then trigger devaluation [wang-2015-uv-existence-explanations](/data/citations.json#wang-2015-uv-existence-explanations). It is consistent with the label effects in Theme 4.

## Theme 3 — Neural correlates {#theme-neural}

| Signal | Finding | Interpretation | Source (verification) |
|---|---|---|---|
| fMRI repetition suppression, anterior intraparietal sulcus (aIPS) | Android > human and robot (bilateral); N = 19 | Prediction error from human appearance plus mechanical motion. The authors say this cannot establish a causal link to the uncanny valley. | saygin-2012-predictive-coding (full text) |
| fMRI, left extrastriate body area | Human and android > robot | Sensitivity to humanlike form | saygin-2012-predictive-coding (full text) |
| 7T fMRI, TPJ | Linear humanlikeness code | Graded human-likeness representation | rosenthal-von-der-putten-2019-vmpfc (full text) |
| 7T fMRI, DMPFC and fusiform | Non-linear human/non-human distinction | Category-like representation | rosenthal-von-der-putten-2019-vmpfc (full text) |
| 7T fMRI, vmPFC | Non-linear likability with a dip for highly humanlike agents; approximated by multiplying TPJ, DMPFC and fusiform signals | Neural value signal for the valley | rosenthal-von-der-putten-2019-vmpfc (full text) |
| 7T fMRI, choice | vmPFC encodes choices among humans; DMPFC among artificial agents; amygdala predicts rejection of artificial agents | The valley carries into social decisions | rosenthal-von-der-putten-2019-vmpfc (full text) |
| ERP N170 | U-shaped over 6 realism levels | Early face encoding is sensitive to both ends | schindler-2017-erp-realism (full text) |
| ERP EPN | No realism effect (emotion only) | — | schindler-2017-erp-realism (full text) |
| ERP LPP | Linear increase with realism | No affective "dip" | schindler-2017-erp-realism (full text) |
| EEG decoding | Real vs AI faces reliably decoded; behaviour near chance | Implicit sensitivity without explicit access | moshel-2022-decoding-ai-faces (abstract) |
| ERP N170 / N400 / EPN / LPP, real vs AI faces | First three differ; LPP does not (N = 28, exploratory) | Early-stage differentiation | mamsheli-2026-eeg-ai-faces (abstract) |
| ERP, label manipulation | Smiles labelled fake lose P1/N170/EPN smile effects; LPP effect appears only for "fake" smiles | Belief blunts early affective processing and adds effortful evaluation | eiserbeck-2023-deepfake-smiles (full text) |

**Caveat:** samples are small (19–33), there are few replications, and stimulus sets differ.

## Theme 4 — Synthetic faces and deepfakes: crossing the valley {#theme-deepfakes}

**On conscious judgments, modern GAN faces are past the valley:**

- Accuracy on StyleGAN2 faces was 48.2% (95% CI 47.1–49.2) [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022).
- Synthetic faces were rated more trustworthy: 4.82 vs 4.48, +7.7%, d = 0.49 [nightingale-farid-2022](/data/citations.json#nightingale-farid-2022).
- White AI faces were judged human 65.9% of the time vs 51.1% for real faces [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism).

**Why this fits the mechanism evidence (inference linking two literatures):**

- The uncanny response is driven by distortion and mismatch (F3).
- GAN faces are internally consistent and close to the population average, so they lack the trigger.
- Their averageness is then misread as a sign of a real person. Participants misjudged proportions, familiarity and memorability [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism).
- Direct support for the averageness link: in two identity-trained neural networks, StyleGAN2 faces sat closer to the centre of face space than real faces. Super-recognisers used that centrality as a cue to AI and typical observers did not [dunn-2026-super-recognizers-average](/data/citations.json#dunn-2026-super-recognizers-average).
- No single study tests the whole chain.

**Residual signals exist but are weak or unusable:**

- EEG reliably separates real from AI faces while group-level judgments are near chance, with frequent label swaps [moshel-2022-decoding-ai-faces](/data/citations.json#moshel-2022-decoding-ai-faces).
- A classifier on 14 human-rated attributes reached 94%. The cues are present but misweighted [miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism).
- Disrupting face processing hurt human deepfake-video detection but mostly not the model's. Human detection relies on specialised face processing [groh-2022-crowds-machines](/data/citations.json#groh-2022-crowds-machines).
- AI faces were recognised (remembered) significantly better than real faces, yet recognition accuracy did not correlate with real-vs-AI classification accuracy; the authors suggest the advantage is partly covert [sulimani-2026-recognition-ai-faces](/data/citations.json#sulimani-2026-recognition-ai-faces). This sits uneasily with the view that AI faces are simply less memorable; generator and N are not in the abstract.
- Self-resembling deepfake talking heads (2020-era) still produced uncanniness that reduced trust [weisman-pena-2021-doppelganger](/data/citations.json#weisman-pena-2021-doppelganger) (unverified).

**Belief versus pixels.** In Eiserbeck et al. (2023), all 540 photographs were real; only the on-screen label "REAL" or "FAKE" varied [eiserbeck-2023-deepfake-smiles](/data/citations.json#eiserbeck-2023-deepfake-smiles):

- Happy faces labelled fake were rated less positive (73.12 vs 74.97) and took longer to rate (≈1,366 vs ≈1,290 ms).
- Their early neural smile effects (P1, N170, EPN) disappeared, and a late LPP effect appeared that was absent for real-labelled smiles.
- Angry and neutral faces were unaffected by the label.
- Disclosure therefore changes affective response even when nothing about the image changes. This works in both directions: falsely labelling real content as AI also devalues it.

**Voices: realism parity, no hyperrealism.** Clones were rated as real as the speakers they copied (63.0 vs 63.8), not more; generic AI voices were rated less real (47.4) [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism). See MR-0003.

**Motion and voice are open questions.** The prediction-error findings (F6) suggest that motion or audio-visual mismatch could re-open the valley for video deepfakes. This has not been tested with current real-time or diffusion video generators.

## Practical reading for detection {#detection-implications}

Editorial inference, not tested interventions:

- **Gut-level uncanniness is no longer a usable signal for high-quality still faces** (F9, F10). The *absence* of uncanniness is not evidence of authenticity.
- **Mismatch is still the trigger** (F3). Cheaper or real-time deepfakes are most likely to give themselves away through lip-sync errors, unusual blinking or gaze, inconsistent lighting, or audio-visual lag. These cues disappear as generators improve.
- **Labels are powerful in both directions** (F11). Provenance and disclosure systems such as C2PA shape how people respond, and false "AI" labels can devalue genuine content.

## Evidence gaps {#gaps}

1. No preregistered, large-N implicit or neural studies with current diffusion-model faces.
2. No studies of uncanny responses to 2025–2026 video deepfakes or real-time face swaps in video calls.
3. Little work on synthetic voice; no robust voice valley has been established. Voice studies are not covered in v0.1.0.
4. Cross-race tests of hyperrealism with less biased generators are missing.
5. Whether people can be trained to use their implicit unease is untested.

## Answers to common questions {#faq}

**Q: Is the uncanny valley real?**
A: Yes. Across 72 studies the effect is large (g = 1.01). It is conditional: it is driven by distortion or mismatch, not by human likeness alone (MR-0002.F1, F2).

**Q: What causes the uncanny valley?**
A: The best-supported cause is perceptual mismatch or distortion. Category ambiguity is contradicted. Attributing feelings to machines is a separate route (F3–F5).

**Q: Do deepfakes fall into the uncanny valley?**
A: High-quality synthetic still faces do not. They are judged at chance, trusted more, and can look more real than real faces (F9). Imperfect video deepfakes may still trigger uncanniness (F12).

**Q: Can your brain detect deepfakes even if you can't?**
A: Partly. EEG reliably distinguished AI from real faces while people's judgments were near chance (F10). The signal does not reach conscious decisions.

**Q: Does knowing a face is AI change how we feel about it?**
A: Yes. Labelling a real smiling face as "fake" made it rated less positive and removed early neural smile responses (F11).

## Method of this metareport {#method}

- **Type:** narrative synthesis across uncanny-valley research (robots, CG, VR, androids) and synthetic-face research. No new statistical pooling.
- **Confidence ratings** are editorial, based on consistency, sample size, tier and verification status.
- **Search limits for v0.1.0:** compiled with restricted web access, so 2024–2026 literature on deepfake video and synthetic voice is under-covered. v0.3.0 (2026-10-18) added one 2026 face-memory study found via PubMed.

## Verification status {#verification}

| Status | Count | Sources |
|---|---|---|
| `verified-fulltext` | 8 | saygin-2012-predictive-coding, rosenthal-von-der-putten-2019-vmpfc, schindler-2017-erp-realism, eiserbeck-2023-deepfake-smiles, nightingale-farid-2022, miller-2023-ai-hyperrealism, dunn-2026-super-recognizers-average, lavan-2025-voice-clones-hyperrealism |
| `verified-abstract` | 11 | mathur-reichling-2016-cartography, katsyri-2015-uv-review, diel-2022-uv-meta, macdorman-chattopadhyay-2016, cheetham-2014-happy-valley, moshel-2022-decoding-ai-faces, groh-2022-crowds-machines, sulimani-2026-recognition-ai-faces, mamsheli-2026-eeg-ai-faces, hsieh-2026-robot-gaze-uncanny, jang-2026-uncanny-conversation |
| `verified-doi` | 2 | burleigh-2013-does-uv-exist, gray-wegner-2012-mind-perception |
| `unverified` | 7 | mori-2012-uncanny-valley, mathur-2020-uncanny-not-confusing, seyama-nagayama-2007, steckenfinger-ghazanfar-2009, wang-2015-uv-existence-explanations, stein-ohler-2017-uncanny-mind, weisman-pena-2021-doppelganger |

Values from `unverified` records are marked "≈" or "unverified" in this report. Check them against the primary source before high-stakes use. Each source page states exactly what was checked and how.

## References {#references}

- [burleigh-2013-does-uv-exist](/data/citations.json#burleigh-2013-does-uv-exist) · [record](/sources/burleigh-2013-does-uv-exist.md) · Burleigh, T. J., et al. (2013). Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces. *Computers in Human Behavior*, 29(3), 759-771. · https://doi.org/10.1016/j.chb.2012.11.021
- [cheetham-2014-happy-valley](/data/citations.json#cheetham-2014-happy-valley) · [record](/sources/cheetham-2014-happy-valley.md) · Cheetham, M., et al. (2014). Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'. *Frontiers in Psychology*, 5, 1219. · https://doi.org/10.3389/fpsyg.2014.01219
- [diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta) · [record](/sources/diel-2022-uv-meta.md) · Diel, A., et al. (2022). A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables. *ACM Transactions on Human-Robot Interaction*, 11(1), Article 1. · https://doi.org/10.1145/3470742
- [dunn-2026-super-recognizers-average](/data/citations.json#dunn-2026-super-recognizers-average) · [record](/sources/dunn-2026-super-recognizers-average.md) · Dunn, J. D., et al. (2026). Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it. *British Journal of Psychology*. · https://doi.org/10.1111/bjop.70063
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- [mamsheli-2026-eeg-ai-faces](/data/citations.json#mamsheli-2026-eeg-ai-faces) · [record](/sources/mamsheli-2026-eeg-ai-faces.md) · Mamsheli, S. T., et al. (2026). Processing of Real and AI-Generated Human Faces: An Exploratory EEG Study. *Proceedings of the European Conference on Cognitive Ergonomics (ECCE 2026)*, 375-379. · https://doi.org/10.1145/3822301.3822351
- [mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography) · [record](/sources/mathur-reichling-2016-cartography.md) · Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. *Cognition*, 146, 22-32. · https://doi.org/10.1016/j.cognition.2015.09.008
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- [seyama-nagayama-2007](/data/citations.json#seyama-nagayama-2007) · [record](/sources/seyama-nagayama-2007.md) · Seyama, J., & Nagayama, R. S. (2007). The uncanny valley: Effect of realism on the impression of artificial human faces. *Presence: Teleoperators and Virtual Environments*, 16(4), 337-351. · https://doi.org/10.1162/pres.16.4.337
- [steckenfinger-ghazanfar-2009](/data/citations.json#steckenfinger-ghazanfar-2009) · [record](/sources/steckenfinger-ghazanfar-2009.md) · Steckenfinger, S. A., & Ghazanfar, A. A. (2009). Monkey visual behavior falls into the uncanny valley. *Proceedings of the National Academy of Sciences (PNAS)*, 106(43), 18362-18366. · https://doi.org/10.1073/pnas.0910063106
- [stein-ohler-2017-uncanny-mind](/data/citations.json#stein-ohler-2017-uncanny-mind) · [record](/sources/stein-ohler-2017-uncanny-mind.md) · Stein, J.-P., & Ohler, P. (2017). Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. *Cognition*, 160, 43-50. · https://doi.org/10.1016/j.cognition.2016.12.010
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- [wang-2015-uv-existence-explanations](/data/citations.json#wang-2015-uv-existence-explanations) · [record](/sources/wang-2015-uv-existence-explanations.md) · Wang, S., et al. (2015). The uncanny valley: Existence and explanations. *Review of General Psychology*, 19(4), 393-407. · https://doi.org/10.1037/gpr0000056
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================================================================================
URL: https://research.breacher.ai/metareports/voice-cloning-human-detection.md
================================================================================

---
id: MR-0003
type: metareport
slug: voice-cloning-human-detection
title: 'Voice Clones and Synthetic Speech: How Well Do People Detect Them?'
question: Can people tell cloned or synthetic voices from real speakers, and what changes that ability?
version: 0.1.0
published: 2026-10-19
updated: 2026-10-19
status: seed-partially-verified
method: narrative-synthesis
scope: Peer-reviewed behavioural studies (2023-2026) of human listeners judging synthetic or cloned speech against real speech, plus the audio subset of one meta-analysis. Split out of MR-0001 on 2026-10-19; MR-0001.F12 and F15 remain the cross-modal summary.
sources:
- barrington-2025-voice-clones
- lavan-2025-voice-clones-hyperrealism
- ivory-2026-faces-voices-domain
- mai-2023-speech-deepfakes
- san-segundo-2026-audio-language-style
- groh-2024-political-speech
- diel-2024-deepfake-detection-meta
- cooke-2024-coin-toss
related:
- MR-0001
- MR-0002
tags:
- voice-cloning
- synthetic-speech
- vishing
- human-detection
- audio-deepfakes
verification_counts:
  verified-fulltext: 5
  verified-abstract: 3
  verified-doi: 0
  unverified: 0
canonical_url: https://research.breacher.ai/metareports/voice-cloning-human-detection.md
---

# MR-0003 · Voice Clones and Synthetic Speech: How Well Do People Detect Them?

> **Question:** Can people tell cloned or synthetic voices from real speakers, and what changes that ability?  
> **Answer in one line:** Barely, for modern clones of unfamiliar speakers: a commercial clone was accepted as the same person in a median 83.3% of pairs, listener sensitivity was d′ ≈ 0.1–0.2, and confidence did not track accuracy. Older or generic synthetic voices are easier (≈70% single-clip), and comparing against genuine reference audio helps most.  
> **Version:** 0.1.0 · **Updated:** 2026-10-19 · **Sources:** 8 (5 checked against full text, 3 against the official abstract, 0 DOI-only, 0 unverified; see [Verification status](#verification)).

## TL;DR {#tldr}

- **Clones pass as the person:** real–clone pairs judged the same speaker in a median 83.3% (IQR 69.2–100.0); real–real pairs 100.0% [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones).
- **Near-chance sensitivity:** d′ 0.10–0.13 for ElevenLabs clones [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism); 53.95% correct (d′ .20) [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain); clones identified 60.8% [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones).
- **Bias toward "human":** criterion c = −0.65 for clones [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism); c = −.07 [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain).
- **Realism parity, not hyperrealism:** clones rated as real as human voices (63.0 vs 63.8), not more [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism).
- **Older and generic voices are easier:** VITS TTS 70.35% single clip [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes); generic AI voices d′ 2.93 [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism); TTS fakes caught 72% vs 83% for voice actors [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech).
- **Comparison helps:** picking the fake from a pair 85.59% vs 70.35% single clip; familiarisation only +3.84 points [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes).
- **Confidence is no guide:** metacognitive AUROC .48 for voices [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain).

## Key numbers {#key-numbers}

| Metric | Value | Source | Verification |
|---|---|---|---|
| Real–clone pairs judged same speaker (median) | 83.3% (IQR 69.2–100.0) | barrington-2025-voice-clones | full text |
| Clones / real clips correctly classified | 60.8% / 67.4% | barrington-2025-voice-clones | full text |
| Listener sensitivity to clones (d′), Exp 1b / Exp 2 | 0.10 / 0.13 | lavan-2025-voice-clones-hyperrealism | full text |
| Realness rating, clones vs real (0–100) | 63.0 vs 63.8 (p = .682) | lavan-2025-voice-clones-hyperrealism | full text |
| Generic AI voices labelled human / d′ | 0.39 vs 0.72 for real / 2.93 | lavan-2025-voice-clones-hyperrealism | full text |
| Voice-clone accuracy; d′; at or below chance | 53.95% [52.64, 55.27]; .20; 35.47% of listeners | ivory-2026-faces-voices-domain | full text |
| Metacognitive AUROC, voices vs faces | .48 vs .59 | ivory-2026-faces-voices-domain | full text |
| TTS speech, single clip / pick-the-fake pair | 70.35% / 85.59% | mai-2023-speech-deepfakes | full text |
| Crowd-aggregated AUROC / out-of-domain detector | 95.51% / 25.31% | mai-2023-speech-deepfakes | full text |
| TTS vs voice-actor deepfakes identified | 72% vs 83% | groh-2024-political-speech | full text |
| Synthetic speech, Spanish and Japanese listeners | 59.11% | san-segundo-2026-audio-language-style | abstract |
| Pooled audio accuracy (k = 8) | 62.08% [38.23, 83.18] | diel-2024-deepfake-detection-meta | abstract |

## Key findings (citable) {#findings}

| ID | Finding | Confidence | Supporting sources |
|---|---|---|---|
| MR-0003.F1 | Commercial voice clones of unfamiliar speakers are accepted as the same identity as the real speaker in most pairs (median 83.3%; about 80% in the abstract). | Moderate (one verified large-N study) | barrington-2025-voice-clones |
| MR-0003.F2 | Listener sensitivity to modern voice clones is near chance: d′ 0.10–0.20, with 53.95–60.8% of clones correctly identified across three studies. | High | barrington-2025-voice-clones, lavan-2025-voice-clones-hyperrealism, ivory-2026-faces-voices-domain |
| MR-0003.F3 | Judgments of clones are biased toward "human/real" (c = −0.65; c = −.07), extending the truth default of MR-0001.F6 to voices. | Moderate | lavan-2025-voice-clones-hyperrealism, ivory-2026-faces-voices-domain |
| MR-0003.F4 | Clones reach realism parity with human voices but not hyperrealism (63.0 vs 63.8); generic, non-cloned AI voices remain easy to spot (d′ 2.93) and are rated more dominant. | Low | lavan-2025-voice-clones-hyperrealism |
| MR-0003.F5 | Detectability depends on synthesis method: older TTS is caught on ≈70% of single clips, state-of-the-art TTS less often than voice actors (72% vs 83%), and pooled audio accuracy (62.08%) has a CI spanning chance. | Moderate | mai-2023-speech-deepfakes, groh-2024-political-speech, diel-2024-deepfake-detection-meta, san-segundo-2026-audio-language-style |
| MR-0003.F6 | Comparison beats single judgment: choosing the fake from a pair reached 85.59% vs 70.35% for single clips, and same-sentence reference audio helped significantly; familiarisation (+3.84 points), extra time and repeated listening helped little. | Moderate | mai-2023-speech-deepfakes |
| MR-0003.F7 | Confidence carries no information about voice-clone accuracy (metacognitive AUROC .48), and voice-clone detection skill is not predicted by face-detection skill. | Low | ivory-2026-faces-voices-domain |
| MR-0003.F8 | Listeners rely on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter) and on delivery more than content; breathing cues predicted voice accuracy. | Moderate | san-segundo-2026-audio-language-style, ivory-2026-faces-voices-domain, groh-2024-political-speech |
| MR-0003.F9 | Aggregating many listeners is robust (AUROC 95.51%) where an out-of-domain automated detector failed (25.31%). | Low | mai-2023-speech-deepfakes |

Confidence (editorial, GRADE-inspired): **High** = consistent across ≥3 independent peer-reviewed studies, or one verified large-N study with internal replications; **Moderate** = fewer studies or heterogeneity; **Low** = one study or unverified details.

## Evidence table: accuracy by study {#evidence-table}

Accuracy here means the percentage of correct real-vs-synthetic judgments; chance is 50%.

| Source ID | Year | Modality | Stimuli | N | Headline result | Tier | Verification |
|---|---|---|---|---|---|---|---|
| barrington-2025-voice-clones | 2025 | audio | ElevenLabs clones of 220 speakers | 634 | same speaker 83.3%; clones caught 60.8% | B | full text |
| lavan-2025-voice-clones-hyperrealism | 2025 | audio | ElevenLabs clones + generic AI voices | 150 | clones d′ 0.10–0.13; generic d′ 2.93 | B | full text |
| ivory-2026-faces-voices-domain | 2026 | audio, face image | ElevenLabs clones; Firefly faces | 172 | voices 53.95% vs faces 65.48% | B | full text |
| mai-2023-speech-deepfakes | 2023 | audio | VITS TTS (EN, ZH) | 529 | 70.35% single; 85.59% paired | B | full text |
| groh-2024-political-speech | 2024 | audio, video, text | TTS vs voice actors | 2,215 | TTS 72% vs actors 83% | B | full text |
| san-segundo-2026-audio-language-style | 2026 | audio | Spanish/Japanese synthetic speech | 54 | 59.11% | B | abstract |
| diel-2024-deepfake-detection-meta | 2024 | audio subset | k = 8 | — | 62.08% [38.23, 83.18] | A | abstract |
| cooke-2024-coin-toss | 2025 | mixed incl. audio | 2023 generators | 1,276 | near 50%; foreign-language and single-modality items harder | B | abstract |

## Chart: correct real-vs-synthetic judgments by study {#chart-accuracy}

Text equivalent: VITS TTS pairs 85.59% and single clips 70.35% [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes); TTS political speech 72% [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech); pooled audio 62.08% [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta); ElevenLabs clones identified 60.8% [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones); Spanish/Japanese synthetic speech 59.11% [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style); ElevenLabs clones 53.95% [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain). The line marks 50% chance. Newer cloning tools sit closest to chance.

```mermaid
xychart-beta
    title "Human detection of synthetic speech by study (% correct)"
    x-axis ["TTS pair", "TTS single", "TTS speech", "Pooled audio", "Clones (B)", "ES/JA", "Clones (I)"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [85.59, 70.35, 72, 62.08, 60.8, 59.11, 53.95]
    line [50, 50, 50, 50, 50, 50, 50]
```

## Theme 1 — Clones pass as the real speaker {#theme-identity}

- **Identity:** in 304 listeners' same/different judgments, real–clone pairs were judged the same speaker in a median 83.3% of trials, close to real–real pairs (100.0%) and far above different real speakers (11.1%); 26.6% of listeners said "same" on every real–clone trial [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones).
- **Realness:** clones were labelled human about as often as real voices (0.58 vs 0.62; 0.70 vs 0.72) and rated as real (63.0 vs 63.8), so they are realistic but not hyperrealistic [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism). Compare AI faces in MR-0001.F2.
- **Scope:** every clone study used unfamiliar speakers and one commercial service (ElevenLabs).

## Theme 2 — Sensitivity is near chance and biased toward "human" {#theme-sensitivity}

- **d′ ≈ 0.1–0.2:** 0.10 and 0.13 across experiments [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism); .20, with 35.47% of listeners at or below chance [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain); 21.0% at or below chance on clones [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones).
- **Criterion:** c = −0.65 toward "human" for clones [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism); c = −.07 for voices [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain).
- **Trust and dominance:** clones were not rated more trustworthy than real voices (58.9 vs 57.1, n.s.) but were rated more dominant (49.4 vs 44.0) [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism).

## Theme 3 — Synthesis method and language {#theme-method}

- **Older TTS:** VITS fakes 70.35% single clip (73% hit rate on fakes, 67.78% on genuine); English and Mandarin listeners similar (+1.79 points, n.s.) [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes).
- **TTS vs human impersonators:** state-of-the-art TTS fakes were identified 72% of the time vs 83% for voice-actor fakes; audio alone 81% in Exp 1a [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech).
- **Generic vs cloned AI voices:** generic designed voices were easy (d′ 2.93); clones were not [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism).
- **Language:** Spanish and Japanese listeners averaged 59.11%, better on authentic samples, with shared and language-specific cues [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style). Foreign-language items lowered accuracy in a US sample [cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss).
- **Pooled:** audio 62.08% with a CI spanning chance (k = 8) [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta).

## Theme 4 — What helps: comparison, crowds, unscripted speech {#theme-helps}

| Intervention or condition | Effect | Source (verification) |
|---|---|---|
| Pick the fake from a real/fake pair | 85.59% vs 70.35% single clip | mai-2023-speech-deepfakes (full text) |
| Same-sentence reference audio | significant gain (p < 0.001), rarely available in practice | mai-2023-speech-deepfakes (full text) |
| Familiarisation with 5 example clips | +3.84 points | mai-2023-speech-deepfakes (full text) |
| Extra time, repeated listening | little | mai-2023-speech-deepfakes (full text) |
| Aggregating many listeners | AUROC 95.51% | mai-2023-speech-deepfakes (full text) |
| Unscripted rather than scripted clips (exploratory, n = 30) | median 76.7% vs 56.7% | barrington-2025-voice-clones (full text) |

**Pattern:** procedures that give the listener a genuine comparison help far more than awareness or practice. No study has tested training against modern clones.

## Theme 5 — Confidence and cues {#theme-cues}

- **Confidence:** metacognitive AUROC .48 for voices (below chance) vs .59 for faces in the same people; face-detection d′ did not significantly predict voice d′ (β = .21, p = .053) [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain).
- **Cues:** listeners relied on suprasegmental and extralinguistic cues (intonation, rhythm, fluency, pauses, speed, breathing, laughter) [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style); breathing predicted voice accuracy [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain); people relied more on how something was said than on what was said [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech).
- **Machines:** in-domain speech detectors reached AUROC 100% but 25.31% out-of-domain, flagging genuine speech as fake [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes).

## Moderators summary {#moderators}

| Moderator | Direction | Sources |
|---|---|---|
| Synthesis method | clones ≈ chance; older TTS ≈70%; generic AI voices easy | lavan-2025-voice-clones-hyperrealism, mai-2023-speech-deepfakes, groh-2024-political-speech |
| Paired vs single judgment | paired much easier | mai-2023-speech-deepfakes |
| Scripted vs unscripted speech | unscripted easier (exploratory) | barrington-2025-voice-clones |
| Listener language | EN vs ZH n.s.; ES vs JA share most cues; foreign language harder | mai-2023-speech-deepfakes, san-segundo-2026-audio-language-style, cooke-2024-coin-toss |
| Speaker familiarity | untested for clones; political-speech studies used well-known speakers (audio 81%) | groh-2024-political-speech |

## Contradictions and caveats {#caveats}

- Accuracies from 53.95% to 85.59% reflect task (single vs paired, generator, language), not disagreement.
- Mai et al. found confidence-weighted AUROC of 73.83%, so confidence carried some signal for older TTS; for clones it did not [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain).
- All studies warned listeners that fakes were present; real-world detection is likely lower.

## Evidence gaps {#gaps}

1. No peer-reviewed study of clones of **familiar** voices (a colleague, a family member, an executive) was found.
2. No study tests telephone-band audio, background noise or live two-way conversation (vishing conditions).
3. No study of real-time voice conversion in calls.
4. No training study against modern clones, and no retention data.
5. One cloning service (ElevenLabs) dominates the evidence.

## Implications (editorial inference, not study findings) {#implications}

- **Do not authenticate by voice.** A call that "sounds like" the person is not evidence (F1, F2, F7).
- **Use callbacks and out-of-band checks** rather than listening harder; comparison with a known-genuine channel is the only listener-side aid with a large effect (F6).
- **Expect a "human" default** in staff judgments (F3).
- **Do not rely on one off-the-shelf audio detector** outside its training domain (F9).

## Answers to common questions {#faq}

**Q: Can people tell a cloned voice from the real person?**
A: Mostly not, for unfamiliar speakers: same speaker in a median 83.3% of pairs [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones); d′ ≈ 0.1 [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism) (MR-0003.F1, F2).

**Q: Are AI voices more convincing than real ones?**
A: Not yet: clones are rated as real as human voices, not more (MR-0003.F4).

**Q: What helps people spot a fake voice?**
A: A genuine reference to compare against (85.59% vs 70.35%) [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes); practice and extra time help little (MR-0003.F6).

**Q: Does it help to know the speaker?**
A: Untested for modern clones; this is the main gap (see [Gaps](#gaps)).

## Method of this metareport {#method}

- **Type:** narrative synthesis; pooled audio value quoted from Diel et al. (2024).
- **Inclusion:** peer-reviewed studies of human listeners judging synthetic or cloned speech against real speech.
- **Origin:** split from MR-0001 on 2026-10-19 because MR-0001 exceeded its page-size limit; MR-0001.F12 and F15 remain valid and cross-reference this report.
- **Search limits:** discovery via OpenAlex, PubMed and reference lists, without general web search. 2025–2026 coverage is incomplete.

## Verification status {#verification}

| Status | Count | Sources |
|---|---|---|
| `verified-fulltext` | 5 | barrington-2025-voice-clones, lavan-2025-voice-clones-hyperrealism, ivory-2026-faces-voices-domain, mai-2023-speech-deepfakes, groh-2024-political-speech |
| `verified-abstract` | 3 | san-segundo-2026-audio-language-style, diel-2024-deepfake-detection-meta, cooke-2024-coin-toss |
| `verified-doi` | 0 | — |
| `unverified` | 0 | — |

Values from `unverified` records are marked "≈" or "unverified" in this report. Check them against the primary source before high-stakes use. Each source page states exactly what was checked and how.

## References {#references}

Source pages contain the full structured records: design, N, results, limitations and verification.

- [barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones) · [record](/sources/barrington-2025-voice-clones.md) · Barrington, S., et al. (2025). People are poorly equipped to detect AI-powered voice clones. *Scientific Reports*, 15, 11004. · https://doi.org/10.1038/s41598-025-94170-3
- [cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss) · [record](/sources/cooke-2024-coin-toss.md) · Cooke, D., et al. (2025). As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli. *Communications of the ACM*, 68, 100-109. · https://doi.org/10.1145/3729417
- [diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta) · [record](/sources/diel-2024-deepfake-detection-meta.md) · Diel, A., et al. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. *Computers in Human Behavior Reports*, 16, 100538. · https://doi.org/10.1016/j.chbr.2024.100538
- [groh-2024-political-speech](/data/citations.json#groh-2024-political-speech) · [record](/sources/groh-2024-political-speech.md) · Groh, M., et al. (2024). Human detection of political speech deepfakes across transcripts, audio, and video. *Nature Communications*, 15, 7629. · https://doi.org/10.1038/s41467-024-51998-z
- [ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain) · [record](/sources/ivory-2026-faces-voices-domain.md) · Ivory, M. R., et al. (2026). Human detection of AI-generated faces and voices is not domain-general. *Scientific Reports*, 16, 28639. · https://doi.org/10.1038/s41598-026-59364-3
- [lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism) · [record](/sources/lavan-2025-voice-clones-hyperrealism.md) · Lavan, N., et al. (2025). Voice clones sound realistic but not (yet) hyperrealistic. *PLOS ONE*, 20(9), e0332692. · https://doi.org/10.1371/journal.pone.0332692
- [mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes) · [record](/sources/mai-2023-speech-deepfakes.md) · Mai, K. T., et al. (2023). Warning: Humans cannot reliably detect speech deepfakes. *PLOS ONE*, 18(8), e0285333. · https://doi.org/10.1371/journal.pone.0285333
- [san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style) · [record](/sources/san-segundo-2026-audio-language-style.md) · San Segundo, E., et al. (2026). Human perception of audio deepfakes: the role of language and speaking style. *Speech Communication*, 185, 103487. · https://doi.org/10.1016/j.specom.2026.103487


================================================================================
URL: https://research.breacher.ai/sources/nightingale-farid-2022.md
================================================================================

---
type: source
id: nightingale-farid-2022
doi: 10.1073/pnas.2120481119
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#nightingale-farid-2022
---

# AI-synthesized faces are indistinguishable from real faces and more trustworthy

> **Source record** `nightingale-farid-2022` · 2022 · Proceedings of the National Academy of Sciences (PNAS)  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** StyleGAN2 faces were classified at chance (48.2%); training+feedback raised accuracy only to 59.0%; synthetic faces were rated 7.7% more trustworthy than real faces.

## Citation {#citation}

Sophie J. Nightingale, Hany Farid (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. *Proceedings of the National Academy of Sciences (PNAS)*, 119(8), e2120481119. https://doi.org/10.1073/pnas.2120481119

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#nightingale-farid-2022](/data/citations.json#nightingale-farid-2022)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 3 online experiments: real-vs-synthetic classification (Exp1), same with training + trial-by-trial feedback (Exp2), trustworthiness ratings 1-7 (Exp3) |
| Modality | image-face |
| Stimuli | 400 StyleGAN2 faces + 400 real faces matched 1:1 from a 70,000-face database (FFHQ-style); balanced 200 women/200 men; 100 each Black, White, East Asian, South Asian |
| Participants (N) | 757 |
| N detail | Exp1 n=315; Exp2 n=219; Exp3 n=223; each judged 128 faces |
| Topics | human-detection, hyperrealism, trust, training |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Exp1 accuracy (no training) | 48.2% [95% CI 47.1, 49.2] | d'=-0.09, beta=0.99 (no response bias) |
| Exp2 accuracy (training + feedback) | 59.0% [95% CI 57.7, 60.4] | d'=0.46; no improvement across session (first half 59.3%, second half 58.8%) |
| Exp3 trustworthiness (1-7) | synthetic 4.82 vs real 4.48 | +7.7% for synthetic; t(222)=14.6, p<0.001, d=0.49 |
| Gender effect on trust | women 4.94 vs men 4.36 | d=0.82 |
| Smiling confound check | 65.5% real vs 58.8% synthetic faces smiling | smiling does not explain higher synthetic trust |

## Moderators {#moderators}

- White synthetic faces were classified least accurately; male White faces worse than female White faces. Authors attribute this to over-representation of White faces in StyleGAN2 training data.
- Feedback produced a one-off gain but no within-session learning, implying some synthetic faces carry no detectable artifacts.

## Limitations {#limitations}

- Single generator (StyleGAN2)
- Curated, artifact-screened stimuli
- Context-free still faces
- Online samples

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Europe PMC full text PMC8872790 |


================================================================================
URL: https://research.breacher.ai/sources/miller-2023-ai-hyperrealism.md
================================================================================

---
type: source
id: miller-2023-ai-hyperrealism
doi: 10.1177/09567976231207095
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#miller-2023-ai-hyperrealism
---

# AI hyperrealism: Why AI faces are perceived as more real than human ones

> **Source record** `miller-2023-ai-hyperrealism` · 2023 · Psychological Science  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** White AI faces were judged human 65.9% of the time vs 51.1% for real White faces ('AI hyperrealism'); the worst detectors were the most confident; a classifier on human-rated attributes reached 94%.

## Citation {#citation}

Elizabeth J. Miller, Ben A. Steward, Zak Witkower, Clare A. M. Sutherland, Eva G. Krumhuber, Amy Dawel (2023). AI hyperrealism: Why AI faces are perceived as more real than human ones. *Psychological Science*, 34(12), 1390-1403. https://doi.org/10.1177/09567976231207095

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#miller-2023-ai-hyperrealism](/data/citations.json#miller-2023-ai-hyperrealism)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Reanalysis of Nightingale & Farid (2022) data; Exp1 real-vs-AI judgments with confidence (White faces, White US participants); Exp2 ratings of 14 face attributes + lens-model analysis and random-forest classifier |
| Modality | image-face |
| Stimuli | White and non-White StyleGAN2 faces and human faces from the Nightingale & Farid (2022) set |
| Participants (N) | 734 |
| N detail | Exp1 n=124 (White US adults); Exp2 n=610; reanalysis ≈315 (Nightingale & Farid Exp1) |
| Topics | human-detection, hyperrealism, overconfidence, uncanny-valley |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Exp1: White AI faces judged human | 65.9% |  |
| Exp1: White human faces judged human | 51.1% |  |
| Reanalysis of Nightingale & Farid: White AI vs human judged human | 69.5% vs 52.2% | non-White faces ≈ chance, no significant AI-human difference |
| Confidence vs errors (AI faces) | r = .385 | more errors ↔ higher confidence (Dunning-Kruger-like) |
| Negative meta-d' | 59% of participants | poor insight into own errors |
| Random forest on 14 human-rated attributes | 94% [95% CI 91, 97] | cross-validated; cues exist but humans misuse them |

## Moderators {#moderators}

- Participants misread facial proportions, familiarity and memorability as signs of humanness; attractiveness and symmetry were used correctly.
- Authors attribute White-only hyperrealism to StyleGAN2 training data being ≈69% White, making White AI faces more average.

## Limitations {#limitations}

- White participants only in Exp1
- StyleGAN2 only
- Stimuli reused from Nightingale & Farid

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | journals.sagepub.com article page |


================================================================================
URL: https://research.breacher.ai/sources/diel-2024-deepfake-detection-meta.md
================================================================================

---
type: source
id: diel-2024-deepfake-detection-meta
doi: 10.1016/j.chbr.2024.100538
evidence_tier: A
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#diel-2024-deepfake-detection-meta
---

# Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers

> **Source record** `diel-2024-deepfake-detection-meta` · 2024 · Computers in Human Behavior Reports  
> **Evidence tier:** A (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Pooled human deepfake-detection accuracy was 55.54% (95% CI 48.87-62.10), not significantly above chance; detection-improvement strategies raised it to 65.14%.

## Citation {#citation}

Alexander Diel, Tania Lalgi, Isabel Carolin Schröter, Karl F. MacDorman, Martin Teufel, Alexander Bäuerle (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. *Computers in Human Behavior Reports*, 16, 100538. https://doi.org/10.1016/j.chbr.2024.100538

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#diel-2024-deepfake-detection-meta](/data/citations.json#diel-2024-deepfake-detection-meta)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Systematic review and meta-analysis of human deepfake-detection studies (k = 137 effect sizes) |
| Modality | image-face, video, audio, text |
| Stimuli | Heterogeneous across included studies |
| Participants (N) | 86155 |
| N detail | 56 papers; 137 effect sizes |
| Topics | human-detection, meta-analysis, modality, training |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Overall accuracy | 55.54% [48.87, 62.10] | k=67; CI includes 50% |
| Audio | 62.08% [38.23, 83.18] | k=8 |
| Video | 57.31% [47.80, 66.57] | k=26 |
| Image | 53.16% [42.12, 64.64] | k=18 |
| Text | 52.00% [37.42, 65.88] | k=15 |
| Odds-ratio analysis | OR 0.64 [0.52, 0.79], ≈39% detection | k=62 |
| With improvement strategies (feedback training, AI support, caricaturization) | 65.14% [55.21, 74.46] | k=15; strongest for video |
| Sensitivity (d') | not significantly different from chance |  |

## Moderators {#moderators}

- Detection-improvement strategies raised performance above chance, with strongest effects for video.

## Limitations {#limitations}

- Heterogeneous paradigms and generators
- Many included studies use pre-2022 generators
- Wide CIs, especially audio

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract; DOI resolves to Elsevier PII S2451958824001714 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/kobis-2021-fooled-twice.md
================================================================================

---
type: source
id: kobis-2021-fooled-twice
doi: 10.1016/j.isci.2021.103364
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#kobis-2021-fooled-twice
---

# Fooled twice: People cannot detect deepfakes but think they can

> **Source record** `kobis-2021-fooled-twice` · 2021 · iScience  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** People could not reliably detect deepfake videos, were biased toward judging them authentic, overestimated their ability, and neither incentives nor awareness raising helped.

## Citation {#citation}

Nils C. Köbis, Barbora Doležalová, Ivan Soraperra (2021). Fooled twice: People cannot detect deepfakes but think they can. *iScience*, 24(11), 103364. https://doi.org/10.1016/j.isci.2021.103364

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#kobis-2021-fooled-twice](/data/citations.json#kobis-2021-fooled-twice)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Pre-registered online behavioural experiment; real vs deepfake video classification; manipulations of financial incentives and awareness; confidence ratings |
| Modality | video |
| Stimuli | Face-swap deepfake videos and authentic videos |
| Participants (N) | 210 |
| Topics | human-detection, overconfidence, response-bias, incentives |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Detection | not reliable | exact accuracy not yet checked |
| Response bias | deepfakes mistaken for authentic more than the reverse | 'seeing-is-believing' heuristic |
| Financial incentives | no improvement |  |
| Awareness raising | no improvement |  |
| Calibration | overconfident |  |

## Limitations {#limitations}

- Single generator family (2020-era face swaps)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/groh-2022-crowds-machines.md
================================================================================

---
type: source
id: groh-2022-crowds-machines
doi: 10.1073/pnas.2110013119
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#groh-2022-crowds-machines
---

# Deepfake detection by human crowds, machines, and machine-informed crowds

> **Source record** `groh-2022-crowds-machines` · 2022 · Proceedings of the National Academy of Sciences (PNAS)  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Ordinary human crowds and the leading DFDC model were similarly accurate but made different mistakes; model-informed participants beat either alone on average, but inaccurate model predictions often lowered their accuracy.

## Citation {#citation}

Matthew Groh, Ziv Epstein, Chaz Firestone, Rosalind Picard (2022). Deepfake detection by human crowds, machines, and machine-informed crowds. *Proceedings of the National Academy of Sciences (PNAS)*, 119(1), e2110013119. https://doi.org/10.1073/pnas.2110013119

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#groh-2022-crowds-machines](/data/citations.json#groh-2022-crowds-machines)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Two online studies comparing human crowds with the leading DFDC model; machine-informed condition; face-processing disruption manipulations |
| Modality | video |
| Stimuli | Deepfake Detection Challenge (DFDC) videos |
| Participants (N) | 15016 |
| N detail | total across two studies |
| Topics | human-detection, human-machine-teaming, face-processing |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Crowd vs leading model | similarly accurate | different kinds of mistakes |
| Machine-informed crowd | more accurate than either alone | inaccurate model predictions often decreased accuracy |
| Face-processing disruptions | hurt humans, mostly not the model | implicates specialised face processing |

## Limitations {#limitations}

- Self-selected online samples
- 2019-2020 face-swap deepfakes

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | arXiv 2105.06496 abstract (journal ref PNAS 119(1)) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/mai-2023-speech-deepfakes.md
================================================================================

---
type: source
id: mai-2023-speech-deepfakes
doi: 10.1371/journal.pone.0285333
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#mai-2023-speech-deepfakes
---

# Warning: Humans cannot reliably detect speech deepfakes

> **Source record** `mai-2023-speech-deepfakes` · 2023 · PLOS ONE  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Listeners were 70.35% accurate on single clips (73% hit rate on fakes); familiarisation added only ≈3.8 points; crowd-aggregated judgments rivalled in-domain detectors, while out-of-domain detectors failed.

## Citation {#citation}

Kimberly T. Mai, Sergi Bray, Toby Davies, Lewis D. Griffin (2023). Warning: Humans cannot reliably detect speech deepfakes. *PLOS ONE*, 18(8), e0285333. https://doi.org/10.1371/journal.pone.0285333

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#mai-2023-speech-deepfakes](/data/citations.json#mai-2023-speech-deepfakes)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online listening experiment (Prolific); unary (single clip) and binary (pick the fake from a pair) tasks; with vs without familiarisation (5 example deepfakes); English and Mandarin |
| Modality | audio |
| Stimuli | VITS text-to-speech deepfakes vs genuine speech (LJSpeech; Chinese Standard Mandarin Speech Corpus); 50 sentences per language, 100 clips, 2-11 s |
| Participants (N) | 529 |
| Topics | human-detection, voice, training, human-vs-machine |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Unary accuracy (single clip) | 70.35% | deepfakes identified 73%; genuine 67.78% |
| Binary accuracy (pick fake from pair) | 85.59% |  |
| Familiarisation effect | +3.84 points (p=0.001) | unary +3.76, binary +3.85 |
| Confidence-weighted AUROC | 73.83% → 75.54% with familiarisation |  |
| Crowd-aggregated AUROC | 95.51% (94.04% with familiarisation) | comparable to in-domain detectors |
| Automated detectors AUROC | in-domain 100%; out-of-domain 25.31% | out-of-domain flagged genuine speech as fake |
| Mandarin vs English listeners | +1.79 points, n.s. (p=0.202) |  |

## Moderators {#moderators}

- Reference audio of the same sentence helped significantly (p<0.001) but is rarely available in practice.
- Extra time and repeated listening did little to improve performance.

## Limitations {#limitations}

- Older TTS (VITS), not current voice cloning
- Short clips
- Participants knew fakes were present

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | journals.plos.org article page |


================================================================================
URL: https://research.breacher.ai/sources/bray-2023-deepfake-faces.md
================================================================================

---
type: source
id: bray-2023-deepfake-faces
doi: 10.1093/cybsec/tyad011
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#bray-2023-deepfake-faces
---

# Testing human ability to detect 'deepfake' images of human faces

> **Source record** `bray-2023-deepfake-faces` · 2023 · Journal of Cybersecurity  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Accuracy was 62.18%, barely above chance; advice and familiarisation did not significantly help; confidence was high (6.83/10) and unrelated to accuracy.

## Citation {#citation}

Sergi D. Bray, Shane D. Johnson, Bennett Kleinberg (2023). Testing human ability to detect 'deepfake' images of human faces. *Journal of Cybersecurity*, 9(1), tyad011. https://doi.org/10.1093/cybsec/tyad011

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#bray-2023-deepfake-faces](/data/citations.json#bray-2023-deepfake-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online experiment (Prolific); 20 images per participant drawn from 50 fake + 50 real; four conditions: control, familiarisation (20 example deepfakes), one-time advice (10 tell-tale features), advice + reminders |
| Modality | image-face |
| Stimuli | Uncurated StyleGAN2 (FFHQ-trained) faces vs real FFHQ faces |
| Participants (N) | 280 |
| Topics | human-detection, training, overconfidence |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Overall accuracy | 62.18% | 'only just above chance' |
| By condition | control 59.65%, familiarisation 60.54%, one-time advice 64.15%, advice+reminders 64.26% | differences n.s. (ANOVA p>0.1) |
| Per-image accuracy | ≈30% to ≈85% | ≈1 in 5 images correct <50% of the time |
| Mean confidence | 6.83 / 10 | within-person gamma ≈0.06-0.21; between-person n.s. |

## Moderators {#moderators}

- Interventions raised accuracy on fakes but lowered it on real images (more real faces called fake) — a criterion shift, not better discrimination.

## Limitations {#limitations}

- 20 images per participant
- Online sample
- StyleGAN2 only

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | academic.oup.com article page |


================================================================================
URL: https://research.breacher.ai/sources/frank-2024-representative-study.md
================================================================================

---
type: source
id: frank-2024-representative-study
doi: null
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#frank-2024-representative-study
---

# A Representative Study on Human Detection of Artificially Generated Media Across Countries

> **Source record** `frank-2024-representative-study` · 2024 · IEEE Symposium on Security and Privacy (S&P 2024)  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Most participants were effectively guessing; AI-generated media was rated more human-like than real media across all media types and countries.

## Citation {#citation}

Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr, Thorsten Eisenhofer, Asja Fischer, Markus Dürmuth, Thorsten Holz (2024). A Representative Study on Human Detection of Artificially Generated Media Across Countries. *IEEE Symposium on Security and Privacy (S&P 2024)*. https://arxiv.org/abs/2312.05976

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#frank-2024-representative-study](/data/citations.json#frank-2024-representative-study)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online survey with representative samples in USA, Germany, China; audio, image and text; regression on individual predictors |
| Modality | image-face, audio, text |
| Stimuli | State-of-the-art (2023) generated audio, images and text vs human-made counterparts |
| Participants (N) | 3002 |
| Topics | human-detection, cross-cultural, hyperrealism |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Overall | near guessing | abstract gives no percentages |
| AI vs real 'human-like' ratings | AI rated more human-like | all media types, all three countries |
| Predictors | generalised trust, cognitive reflection, self-reported deepfake familiarity significantly influenced decisions | across all media |

## Limitations {#limitations}

- No video
- Fixed stimulus set

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | arXiv 2312.05976 abstract; S&P 2024 venue and DOI not confirmed |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/cooke-2024-coin-toss.md
================================================================================

---
type: source
id: cooke-2024-coin-toss
doi: 10.1145/3729417
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#cooke-2024-coin-toss
---

# As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli

> **Source record** `cooke-2024-coin-toss` · 2025 · Communications of the ACM  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Mean detection was close to 50% chance; accuracy dropped for any synthetic content, foreign-language content, single-modality stimuli and synthetic images of faces; prior knowledge of synthetic media did not help; older participants did worse.

## Citation {#citation}

Di Cooke, Abigail Edwards, Sophia Barkoff, Kathryn Kelly (2025). As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli. *Communications of the ACM*, 68, 100-109. https://doi.org/10.1145/3729417

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#cooke-2024-coin-toss](/data/citations.json#cooke-2024-coin-toss)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online US survey; mixed-modality stimuli (images, audio, video, audiovisual) |
| Modality | image-face, video, audio, audiovisual |
| Stimuli | Mixed 2023-era generators |
| Participants (N) | 1276 |
| N detail | preprint first posted March 2024 (arXiv 2403.16760) |
| Topics | human-detection, modality |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Mean detection | close to chance (50%) | abstract gives no exact figure |
| Lower accuracy when | stimulus contains synthetic content, foreign language, single modality, or a synthetic human face |  |
| Mixed-authenticity audiovisual | harder |  |
| Prior knowledge of synthetic media | no significant effect |  |
| Age | older participants performed worse |  |

## Limitations {#limitations}

- US sample only

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | arXiv 2403.16760 abstract page (journal ref CACM 68, 2025) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/groh-2024-political-speech.md
================================================================================

---
type: source
id: groh-2024-political-speech
doi: 10.1038/s41467-024-51998-z
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#groh-2024-political-speech
---

# Human detection of political speech deepfakes across transcripts, audio, and video

> **Source record** `groh-2024-political-speech` · 2024 · Nature Communications  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Accuracy rose with each added modality (transcript 57% → silent video 64% → audio 81% → video+audio 85-86% in Exp 1a); people relied more on how something was said than on what was said, and TTS fakes were harder to spot than voice-actor fakes.

## Citation {#citation}

Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh, Dong Young Kim, Andrew Lippman, Rosalind Picard (2024). Human detection of political speech deepfakes across transcripts, audio, and video. *Nature Communications*, 15, 7629. https://doi.org/10.1038/s41467-024-51998-z

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#groh-2024-political-speech](/data/citations.json#groh-2024-political-speech)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 5 pre-registered experiments; real vs fabricated political speeches shown as transcript, silent video (± subtitles), audio (± subtitles), or video+audio (± subtitles) |
| Modality | text, audio, video, audiovisual |
| Stimuli | Real and fabricated political speeches; fabricated audio from state-of-the-art TTS or voice actors |
| Participants (N) | 2215 |
| N detail | 2,215 recruited participants across 5 experiments, plus 41,313 non-recruited participants in Exp 1b (Exp 1a recruited n=501) |
| Topics | human-detection, modality, voice |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Exp 1a (recruited, n=501): transcript | 57% | content alone ≈ slightly above chance |
| Exp 1a: silent video | 64% |  |
| Exp 1a: audio | 81% | with or without subtitles; Discussion reports 80.5% |
| Exp 1a: video + audio | 85% (with subtitles), 86% (without) |  |
| Exp 1b (non-recruited, n=41,313): transcript / silent video / audio / video+audio | 43% / 65% / 74% / 82-83% |  |
| Exp 3: transcript / subtitled video / audio / video+audio | 58% / 62% / 65% / 74% | same ordering at lower levels |
| Exp 2: TTS vs voice-actor deepfakes identified | 72% vs 83% | state-of-the-art TTS harder to detect |
| Cue reliance | delivery (audio-visual) > content | authors' conclusion |

## Moderators {#moderators}

- Participants struggled to distinguish a speaker's real audio from TTS trained on that speaker.

## Limitations {#limitations}

- Political figures only (familiar speakers may aid detection)
- Participants knew fakes were present

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | nature.com article page (open access); author list on page truncated, middle authors from compiler knowledge |


================================================================================
URL: https://research.breacher.ai/sources/barrington-2025-voice-clones.md
================================================================================

---
type: source
id: barrington-2025-voice-clones
doi: 10.1038/s41598-025-94170-3
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#barrington-2025-voice-clones
---

# People are poorly equipped to detect AI-powered voice clones

> **Source record** `barrington-2025-voice-clones` · 2025 · Scientific Reports  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Listeners judged a commercial voice clone to be the same person as the real speaker about 80% of the time (median 83.3% of real-clone pairs) and identified clones as AI only 60.8% of the time.

## Citation {#citation}

Sarah Barrington, Emily A. Cooper, Hany Farid (2025). People are poorly equipped to detect AI-powered voice clones. *Scientific Reports*, 15, 11004. https://doi.org/10.1038/s41598-025-94170-3

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#barrington-2025-voice-clones](/data/citations.json#barrington-2025-voice-clones)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online perceptual studies: identity study (same speaker? for real-real, real-clone and different-speaker pairs) and naturalness study (real vs AI classification of single clips); small exploratory follow-up |
| Modality | audio |
| Stimuli | ElevenLabs Instant Voice Cloning clones of 220 real speakers from the DeepSpeak dataset (32 prompts per speaker); identity batches of 44 stimuli |
| Participants (N) | 634 |
| N detail | Identity study n=304; naturalness study n=300 recruited (294 analysed); exploratory follow-up n=30 |
| Topics | human-detection, voice, voice-clone, identity, vishing |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Identity: real-real same-speaker pairs judged same | median 100.0% (IQR 85.7-100.0) |  |
| Identity: real-clone pairs judged same speaker | median 83.3% (IQR 69.2-100.0) | abstract: about 80% of the time; 26.6% of listeners said 'same' on every real-clone trial |
| Identity: different real speakers judged same | median 11.1% |  |
| Naturalness: real clips correctly classified | 67.4% (SD 14.8) |  |
| Naturalness: AI clones correctly classified | 60.8% (SD 16.7) | 21.0% of listeners at or below chance on clones |

## Moderators {#moderators}

- Exploratory follow-up: unscripted clips easier than scripted (median 76.7% vs 56.7%)

## Limitations {#limitations}

- Unfamiliar speakers
- Single commercial cloning service
- Short clips, online listening

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | nature.com full text, doi:10.1038/s41598-025-94170-3 |


================================================================================
URL: https://research.breacher.ai/sources/gray-2025-super-recognizers.md
================================================================================

---
type: source
id: gray-2025-super-recognizers
doi: 10.1098/rsos.250921
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#gray-2025-super-recognizers
---

# Training human super-recognizers' detection and discrimination of AI-generated faces

> **Source record** `gray-2025-super-recognizers` · 2025 · Royal Society Open Science  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Untrained controls labelled only 31% of StyleGAN3 faces as AI (below chance) and super-recognisers 41%; ≈5 minutes of artefact training raised these to 51% and 64%, with no retention test.

## Citation {#citation}

Katie L. H. Gray, Josh P. Davis, Carl Bunce, Eilidh Noyes, Kay L. Ritchie (2025). Training human super-recognizers' detection and discrimination of AI-generated faces. *Royal Society Open Science*, 12(11), 250921. https://doi.org/10.1098/rsos.250921

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#gray-2025-super-recognizers](/data/citations.json#gray-2025-super-recognizers)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Two online experiments (Exp1 single-face detection; Exp2 two-face 2AFC discrimination); super-recognisers vs Prolific and database controls; with vs without ≈5-minute artefact training with 10 feedback trials |
| Modality | image-face |
| Stimuli | 80 StyleGAN3 faces and 80 FFHQ real faces, demographically matched |
| Participants (N) | 664 |
| N detail | Super-recognisers n=283, controls n=381 across conditions after catch-trial exclusions |
| Topics | human-detection, training, individual-differences, hyperrealism |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Exp1 AI faces detected, untrained | super-recognisers 41%; Prolific controls 31%; database controls 30% | d' 0.024 / -0.489 / -0.466: controls below chance |
| Exp1 AI faces detected, trained | super-recognisers 64%; Prolific controls 51% | d' 0.738 / -0.066: only super-recognisers above chance |
| Exp2 2AFC discrimination, untrained | super-recognisers 54%; controls 42% / 42% |  |
| Exp2 2AFC discrimination, trained | super-recognisers 64%; Prolific controls 48% |  |

## Moderators {#moderators}

- Super-recogniser advantage persisted after training; training effect similar in both groups

## Limitations {#limitations}

- No retention test
- Single generator (StyleGAN3)
- Training relies on visible rendering artefacts
- Online testing

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | White Rose eprint 241331 (typeset published PDF) |


================================================================================
URL: https://research.breacher.ai/sources/lu-2023-fake2m.md
================================================================================

---
type: source
id: lu-2023-fake2m
doi: null
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#lu-2023-fake2m
---

# Seeing is not always believing: Benchmarking human and model perception of AI-generated images

> **Source record** `lu-2023-fake2m` · 2023 · NeurIPS 2023 Datasets and Benchmarks Track  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Humans misclassified 38.7% of images; the best model failed on 13% under the same conditions.

## Citation {#citation}

Zeyu Lu, Di Huang, Lei Bai, Jingjing Qu, Chengyue Wu, Xihui Liu, Wanli Ouyang (2023). Seeing is not always believing: Benchmarking human and model perception of AI-generated images. *NeurIPS 2023 Datasets and Benchmarks Track*. https://arxiv.org/abs/2304.13023

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#lu-2023-fake2m](/data/citations.json#lu-2023-fake2m)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Human perception benchmark (HPBench) on AI-generated vs real images; model benchmark (MPBench) on the Fake2M dataset |
| Modality | image-general |
| Stimuli | Text-to-image diffusion outputs vs real photos |
| Participants (N) | not recorded |
| N detail | not stated in abstract |
| Topics | human-detection, text-to-image, human-vs-machine |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Human misclassification rate (HPBench) | 38.7% | ≈61.3% accuracy |
| Best model failure rate (MPBench) | 13% | ≈87% accuracy |

## Limitations {#limitations}

- Not face-specific
- Venue (NeurIPS D&B) from compiler knowledge

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | arXiv 2304.13023 abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/lago-2022-more-real-than-real.md
================================================================================

---
type: source
id: lago-2022-more-real-than-real
doi: 10.1109/MSP.2021.3120982
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#lago-2022-more-real-than-real
---

# More real than real: A study on human visual perception of synthetic faces

> **Source record** `lago-2022-more-real-than-real` · 2022 · IEEE Signal Processing Magazine  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: medium)  
> **Bottom line:** An early report that StyleGAN2 faces were rated as more real than real faces.

## Citation {#citation}

Federica Lago, Cecilia Pasquini, Rainer Böhme, Hélène Dumont, Valérie Goasdoué, Giulia Boato (2022). More real than real: A study on human visual perception of synthetic faces. *IEEE Signal Processing Magazine*, 39(1), 109-116. https://doi.org/10.1109/MSP.2021.3120982

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#lago-2022-more-real-than-real](/data/citations.json#lago-2022-more-real-than-real)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Perceptual experiment rating realism of real vs synthetic faces |
| Modality | image-face |
| Stimuli | StyleGAN2 faces vs real faces |
| Participants (N) | not recorded |
| N detail | not recorded |
| Topics | human-detection, hyperrealism |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Realism ratings | synthetic ≥ real |  |

## Limitations {#limitations}

- Single generator

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | medium |
| Checked on | — |
| Via | compiler knowledge; DOI not resolved |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/tucciarelli-2022-realness.md
================================================================================

---
type: source
id: tucciarelli-2022-realness
doi: null
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#tucciarelli-2022-realness
---

# On the realness of people who do not exist: The social processing of artificial faces

> **Source record** `tucciarelli-2022-realness` · 2022 · iScience  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: medium)  
> **Bottom line:** GAN faces were judged as real as, or more real than, real faces; faces rated more trustworthy were more often judged real.

## Citation {#citation}

Raffaele Tucciarelli, Neza Vehar, Shamil Chandaria, Manos Tsakiris (2022). On the realness of people who do not exist: The social processing of artificial faces. *iScience*, 25.

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#tucciarelli-2022-realness](/data/citations.json#tucciarelli-2022-realness)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Real-vs-artificial classification plus social trait ratings (incl. trustworthiness) |
| Modality | image-face |
| Stimuli | StyleGAN-family faces vs real faces |
| Participants (N) | not recorded |
| N detail | not recorded |
| Topics | human-detection, hyperrealism, trust |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Realness classification | near chance; artificial often judged real |  |
| Trust-realness link | positive | more trustworthy → more likely judged real |

## Limitations {#limitations}

- Details pending verification

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | medium |
| Checked on | — |
| Via | compiler knowledge; DOI unknown |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/iproov-2025-consumer-study.md
================================================================================

---
type: source
id: iproov-2025-consumer-study
doi: null
evidence_tier: E
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#iproov-2025-consumer-study
---

# iProov deepfake blindspot consumer study (industry report)

> **Source record** `iproov-2025-consumer-study` · 2025 · Industry report / press release  
> **Evidence tier:** E (industry) · **Verification:** unverified (compiler confidence: medium)  
> **Bottom line:** Vendor-reported: only ≈0.1% of participants classified every item correctly; older adults performed worse; confidence was high regardless of accuracy.

## Citation {#citation}

iProov (2025). iProov deepfake blindspot consumer study (industry report). *Industry report / press release*.

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#iproov-2025-consumer-study](/data/citations.json#iproov-2025-consumer-study)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online consumer test with mixed real and deepfake images and videos |
| Modality | image-face, video |
| Stimuli | Vendor-selected |
| Participants (N) | 2000 |
| N detail | UK and US consumers (approximate) |
| Topics | human-detection, overconfidence |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| All items correct | ≈0.1% of participants | all-correct is a strict criterion, not mean accuracy |

## Moderators {#moderators}

- Older adults performed worse

## Limitations {#limitations}

- Not peer reviewed
- Vendor has commercial interest
- Methods not fully disclosed

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | medium |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/ivory-2026-faces-voices-domain.md
================================================================================

---
type: source
id: ivory-2026-faces-voices-domain
doi: 10.1038/s41598-026-59364-3
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#ivory-2026-faces-voices-domain
---

# Human detection of AI-generated faces and voices is not domain-general

> **Source record** `ivory-2026-faces-voices-domain` · 2026 · Scientific Reports  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Faces were detected above chance (65.48%) but cloned voices barely (53.95%); face and voice skill were not significantly related, and confidence tracked accuracy for faces but not voices.

## Citation {#citation}

Matthew R. Ivory, Carolyn McGettigan, Helen E. Nuttall, Sophie J. Nightingale (2026). Human detection of AI-generated faces and voices is not domain-general. *Scientific Reports*, 16, 28639. https://doi.org/10.1038/s41598-026-59364-3

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#ivory-2026-faces-voices-domain](/data/citations.json#ivory-2026-faces-voices-domain)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Preregistered within-participant study: real-vs-synthetic classification of faces and of voices, each followed by confidence ratings; signal detection (d', c) and type-II AUROC for metacognition |
| Modality | image-face, audio |
| Stimuli | 20 FFHQ real faces + 20 Adobe Firefly (diffusion) faces; 40 VCTK real speakers and ElevenLabs Instant Voice Cloning clones (each listener heard 20 real + 20 clones of other speakers) |
| Participants (N) | 172 |
| N detail | 200 recruited on Prolific, 172 analysed; ages 18-30 |
| Topics | human-detection, voice, voice-clone, confidence, individual-differences |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Face accuracy (diffusion faces) | 65.48% [95% CI 63.75, 67.21] | d' = .85 |
| Voice-clone accuracy | 53.95% [95% CI 52.64, 55.27] | d' = .20; 35.47% of participants at or below chance (faces 12.80%) |
| Face d' predicting voice d' | beta = .21, p = .053, R² = .02 | not significant; no evidence of a domain-general ability |
| Response criterion c | faces -.24; voices -.07 | both biased toward 'real' |
| Metacognitive AUROC | faces .59 (above chance); voices .48 (below chance) | confidence informative for faces only |

## Moderators {#moderators}

- Skin smoothness was used as an AI cue for faces and predicted accuracy; breathing predicted voice accuracy

## Limitations {#limitations}

- Task difficulty not matched across modalities
- Static faces and short clips only, single play
- Participants warned that stimuli could be synthetic
- Young sample (18-30)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | nature.com full text, doi:10.1038/s41598-026-59364-3 |


================================================================================
URL: https://research.breacher.ai/sources/lavan-2025-voice-clones-hyperrealism.md
================================================================================

---
type: source
id: lavan-2025-voice-clones-hyperrealism
doi: 10.1371/journal.pone.0332692
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#lavan-2025-voice-clones-hyperrealism
---

# Voice clones sound realistic but not (yet) hyperrealistic

> **Source record** `lavan-2025-voice-clones-hyperrealism` · 2025 · PLOS ONE  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Listeners could not tell voice clones from the real speakers (d' 0.10-0.13) and rated them as real as human voices, but clones were not judged more human than humans (no hyperrealism); AI voices were rated more dominant.

## Citation {#citation}

Nadine Lavan, Mairi Irvine, Victor Rosi, Carolyn McGettigan (2025). Voice clones sound realistic but not (yet) hyperrealistic. *PLOS ONE*, 20(9), e0332692. https://doi.org/10.1371/journal.pone.0332692

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#lavan-2025-voice-clones-hyperrealism](/data/citations.json#lavan-2025-voice-clones-hyperrealism)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Three online experiments: generic AI voices vs human (Exp1a), voice clones vs the same humans (Exp1b), both together (Exp2); realness, human-vs-AI classification, trustworthiness and dominance ratings |
| Modality | audio |
| Stimuli | 40 VCTK speakers; 40 ElevenLabs Instant Voice Cloning clones (≈4 min of training audio each); 40 ElevenLabs Voice Design generic voices; one Rainbow Passage sentence each, artefact-screened |
| Participants (N) | 150 |
| N detail | n=50 per experiment; UK native English speakers via Prolific; not preregistered |
| Topics | human-detection, voice, voice-clone, hyperrealism, trust |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Exp1b proportion labelled human: clones vs real | 0.58 vs 0.62 | OR = 0.84, p = .239; d' = 0.10, n.s. |
| Exp2 proportion labelled human: clones vs real | 0.70 vs 0.72 | d' clones 0.13; generic AI voices 0.39 vs 0.72, d' 2.93 |
| Exp2 realness ratings: clones vs real | 63.0 vs 63.8 | p = .682 |
| Dominance: clones vs real | 49.4 vs 44.0 | p = .015; generic AI 58.1 vs 40.4 |
| Trustworthiness: clones vs real | 58.9 vs 57.1 | n.s.; generic AI voices 57.9 vs 53.8, p = .002 |

## Moderators {#moderators}

- Generic (non-cloned) AI voices were easy to spot; clones were not
- Criterion biased toward 'human' for clones (c = -0.65 in Exp2)

## Limitations {#limitations}

- Unfamiliar voices only
- Artefact-screened, idealised stimuli
- One sentence per voice
- Not preregistered
- Context effects across experiments

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | journals.plos.org full text |


================================================================================
URL: https://research.breacher.ai/sources/pehlivanoglu-2026-machines-humans.md
================================================================================

---
type: source
id: pehlivanoglu-2026-machines-humans
doi: 10.1186/s41235-025-00700-y
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#pehlivanoglu-2026-machines-humans
---

# Is this real? Susceptibility to deepfakes in machines and humans

> **Source record** `pehlivanoglu-2026-machines-humans` · 2026 · Cognitive Research: Principles and Implications  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** On StyleGAN2 faces humans were at chance (49%, AUC 0.53) with a strong truth bias while a CNN scored 97%; on videos the pattern reversed: machines near chance, humans better.

## Citation {#citation}

Didem Pehlivanoglu, Mengdi Zhu, Jialong Zhen, Aude A. Gagnon-Roberge, Rebecca K. Kern, Damon Woodard, Brian S. Cahill, Natalie C. Ebner (2026). Is this real? Susceptibility to deepfakes in machines and humans. *Cognitive Research: Principles and Implications*, 11, 3. https://doi.org/10.1186/s41235-025-00700-y

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#pehlivanoglu-2026-machines-humans](/data/citations.json#pehlivanoglu-2026-machines-humans)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Study 1: humans rated 200 faces 1 (fake) to 10 (real) plus individual-difference measures; compared with ResNet-50 CNN and frequency-domain SVM. Study 2: same comparison for deepfake videos |
| Modality | image-face, video |
| Stimuli | Study 1: 300 FFHQ real faces and 300 artefact-screened StyleGAN2 faces (sets of 200 per participant). Study 2: deepfake videos (details not read) |
| Participants (N) | 2203 |
| N detail | Study 1 N=2,203 undergraduates (age 18-58, M 19.64); Study 2 N not read |
| Topics | human-detection, human-vs-machine, truth-bias, confidence, individual-differences |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Study 1 human accuracy (faces) | 49%; mean AUC 0.53 | N=2,203 |
| Study 1 deepfakes judged real | 69% | real judged real 67%: truth bias |
| Study 1 confidence | real 6.77 vs deepfake 4.11 (1-10 realness scale) |  |
| Study 1 machine accuracy | CNN 97%; frequency-domain SVM 79% |  |
| Study 2 (videos) | machines near chance; humans outperformed machines | abstract only; values not read |

## Moderators {#moderators}

- Study 1: no individual-difference measure predicted face discrimination
- Study 2 (abstract): higher analytical thinking, lower positive affect and greater internet skills predicted better video detection

## Limitations {#limitations}

- Undergraduate sample, 75% female
- Single GAN for faces
- Online, uncontrolled viewing
- Study 2 values not checked for this record

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | medium |
| Checked on | 2026-10-12 |
| Via | link.springer.com full text (Study 1 methods and results); Study 2 from abstract only |


================================================================================
URL: https://research.breacher.ai/sources/dunn-2026-super-recognizers-average.md
================================================================================

---
type: source
id: dunn-2026-super-recognizers-average
doi: 10.1111/bjop.70063
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#dunn-2026-super-recognizers-average
---

# Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it

> **Source record** `dunn-2026-super-recognizers-average` · 2026 · British Journal of Psychology  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Super-recognisers detected AI faces modestly above chance (57.3%, d' 0.41) while controls were at chance (50.7%); AI faces sit closer to the centre of face space, and super-recognisers use this 'hyper-averageness' as a cue.

## Citation {#citation}

James D. Dunn, David White, Clare A. M. Sutherland, Elizabeth J. Miller, Ben A. Steward, Amy Dawel (2026). Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it. *British Journal of Psychology*. https://doi.org/10.1111/bjop.70063

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#dunn-2026-super-recognizers-average](/data/citations.json#dunn-2026-super-recognizers-average)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Super-recognisers vs motivated controls classifying real vs AI faces; correlation with face-recognition tests (CFMT+, UNSW Face Test); face-space centrality from two identity DNNs (VGG2, ArcFace) |
| Modality | image-face |
| Stimuli | 100 StyleGAN2 and 100 FFHQ White faces; each participant saw one gender (50 real, 50 AI) |
| Participants (N) | 125 |
| N detail | Super-recognisers n=36, controls n=89 after exclusions |
| Topics | human-detection, individual-differences, hyperrealism, mechanism |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Accuracy: super-recognisers vs controls | 57.3% vs 50.7% | d' 0.41 vs 0.04; t(66.0) = 2.58, p = .012 |
| Face recognition ability vs AI-face sensitivity (controls) | r(87) = .35 [95% CI .15, .52] |  |
| DNN centrality: AI vs real faces | AI faces closer to the face-space centroid | b = -0.210, p < .001 |
| Use of centrality as AI cue | super-recognisers yes; controls n.s. |  |

## Moderators {#moderators}

- Face-recognition ability

## Limitations {#limitations}

- White faces and StyleGAN2 only
- Effect size reported as d = 0.55 in abstract and 0.50 in results

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | UNSWorks repository submitted version (hdl 1959.4/107078) |


================================================================================
URL: https://research.breacher.ai/sources/dawel-2026-training-ai-faces.md
================================================================================

---
type: source
id: dawel-2026-training-ai-faces
doi: 10.1073/pnas.2602122123
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#dawel-2026-training-ai-faces
---

# Training humans to detect AI-generated faces

> **Source record** `dawel-2026-training-ai-faces` · 2026 · Proceedings of the National Academy of Sciences (PNAS)  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Training people to notice the global impressions AI faces systematically evoke (rather than artefacts) improved every participant, roughly doubled mean accuracy, and made confidence well calibrated; a test-retest control ruled out practice effects.

## Citation {#citation}

Amy Dawel, Tanya George, Eric Y. Mah, James D. Dunn, Clare A. M. Sutherland, Nick Argument, James W. Tanaka (2026). Training humans to detect AI-generated faces. *Proceedings of the National Academy of Sciences (PNAS)*, 123(27), e2602122123. https://doi.org/10.1073/pnas.2602122123

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#dawel-2026-training-ai-faces](/data/citations.json#dawel-2026-training-ai-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Pre-post training study on untrained test faces; test-retest control study; online replication |
| Modality | image-face |
| Stimuli | AI-generated and real faces (details not in abstract) |
| Participants (N) | 45 |
| N detail | Main pre-post study N=45; control and online replication Ns not in abstract |
| Topics | human-detection, training, confidence, hyperrealism |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Pre-post accuracy | all participants improved; mean accuracy nearly doubled | exact values not in abstract |
| High performers | near-perfect detection after training |  |
| Confidence | well calibrated only after training |  |
| Controls | test-retest control ruled out practice effects; online replication supported scalability |  |

## Limitations {#limitations}

- Small main sample
- Retention not reported in abstract
- Values not checked against full text

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-12 |
| Via | OpenAlex abstract record for doi:10.1073/pnas.2602122123 (publisher page not reachable) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/rehman-2025-training-synthetic-faces.md
================================================================================

---
type: source
id: rehman-2025-training-synthetic-faces
doi: 10.3389/frai.2025.1568267
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#rehman-2025-training-synthetic-faces
---

# Training humans for synthetic face image detection

> **Source record** `rehman-2025-training-synthetic-faces` · 2025 · Frontiers in Artificial Intelligence  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** One-shot artefact training raised trained participants' accuracy on synthetic faces (79.08% vs 58.29% for controls) but lowered accuracy on real faces, so overall accuracy did not differ significantly between groups.

## Citation {#citation}

Ramlah Sara Rehman, Ewald Meier, Mathias Ibsen, Christian Rathgeb, Robert Nichols, Christoph Busch (2025). Training humans for synthetic face image detection. *Frontiers in Artificial Intelligence*, 8, 1568267. https://doi.org/10.3389/frai.2025.1568267

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#rehman-2025-training-synthetic-faces](/data/citations.json#rehman-2025-training-synthetic-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online randomised experiment: 16 trials, then artefact/semantic-region training (experimental) or a 5-min break (control), then 16 trials |
| Modality | image-face |
| Stimuli | StyleGAN2 and diffusion (dezgo.com) synthetic faces plus FFHQ and online real faces; 8 GAN and 8 diffusion images in total |
| Participants (N) | 184 |
| N detail | Experimental n=95, control n=89 (254 started) |
| Topics | human-detection, training, criterion-shift |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Overall accuracy, experimental pre vs post | 67.43% vs 71.12% | p = 0.01 |
| Overall accuracy, control pre vs post | 68.12% vs 68.33% | p = 0.88 |
| Post-intervention, experimental vs control | difference 2.79 points | p = 0.08, n.s. |
| Post-intervention synthetic faces correct | experimental 79.08% vs control 58.29% | p < 0.001 |
| Post-intervention real faces correct | experimental lower than control (control 78.37%) | t(182) = -5.37, p < 0.001: consistent with a shift toward 'synthetic' |
| Pre-intervention accuracy on GAN vs diffusion fakes | GAN 25.26% / 24.72% vs diffusion 88.68% / 90.45% (experimental / control) | the diffusion fakes used were easy; GAN fakes were mostly judged real |

## Moderators {#moderators}

- Generator: GAN fakes far harder than the diffusion fakes used

## Limitations {#limitations}

- No d' or criterion reported
- 8 images per generator
- Single session, no retention
- Online, uncontrolled

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | frontiersin.org full text |


================================================================================
URL: https://research.breacher.ai/sources/kramer-2024-crowds-synthetic-faces.md
================================================================================

---
type: source
id: kramer-2024-crowds-synthetic-faces
doi: 10.1002/acp.4245
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#kramer-2024-crowds-synthetic-faces
---

# Crowds Improve Human Detection of AI-Synthesised Faces

> **Source record** `kramer-2024-crowds-synthetic-faces` · 2024 · Applied Cognitive Psychology  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Aggregating different people's judgements (outer crowds) improved synthetic-face detection as crowd size grew, with gains comparable to training; averaging one person's two attempts (inner crowd) added little.

## Citation {#citation}

Robin S. S. Kramer, Charlotte Cartledge (2024). Crowds Improve Human Detection of AI-Synthesised Faces. *Applied Cognitive Psychology*, 38(5). https://doi.org/10.1002/acp.4245

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#kramer-2024-crowds-synthetic-faces](/data/citations.json#kramer-2024-crowds-synthetic-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Exp1: real-vs-synthetic ratings on a 1-7 scale, completed twice (inner and outer crowds). Exp2: single binary judgement with confidence and estimate of others' agreement (majority vote, confidence-weighted and 'surprisingly popular' aggregation) |
| Modality | image-face |
| Stimuli | AI-synthesised and real faces (details not in abstract) |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | human-detection, crowds, aggregation |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Inner crowd | little benefit over individuals |  |
| Outer crowd | performance improved with crowd size |  |
| Aggregation rule | majority vote best for small crowds; 'surprisingly popular' best for larger crowds |  |
| Personality | no association with performance |  |

## Moderators {#moderators}

- Crowd size
- Aggregation rule

## Limitations {#limitations}

- Values not checked against full text

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-12 |
| Via | OpenAlex abstract record for doi:10.1002/acp.4245 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/kramer-2026-aggregation-human-ai.md
================================================================================

---
type: source
id: kramer-2026-aggregation-human-ai
doi: 10.1186/s41235-026-00758-2
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#kramer-2026-aggregation-human-ai
---

# Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration

> **Source record** `kramer-2026-aggregation-human-ai` · 2026 · Cognitive Research: Principles and Implications  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Individuals detected deepfake videos above chance but modestly; whole-sample aggregation helped, but confidence did not discriminate correct from incorrect trials, confidence-weighted pairing reduced accuracy, and gains from accurate AI guidance were offset by following incorrect advice.

## Citation {#citation}

Robin S. S. Kramer, Victoria P. M. Lister (2026). Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration. *Cognitive Research: Principles and Implications*. https://doi.org/10.1186/s41235-026-00758-2

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#kramer-2026-aggregation-human-ai](/data/citations.json#kramer-2026-aggregation-human-ai)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Four experiments, real-vs-deepfake video judgements. Exp1: response aggregation across the sample. Exp2: confidence ratings, metacognitive sensitivity, confidence-weighted fusion of participant pairs. Exp3-4: decisions before and after simulated algorithmic guidance, varying guidance accuracy and deepfake prevalence |
| Modality | video |
| Stimuli | YouTube clips and their corresponding deepfakes (generator not in abstract) |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | human-detection, crowds, aggregation, metacognition, human-ai-teaming |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Individual accuracy | above chance but modest and error-prone | no values in abstract |
| Aggregation across full sample (Exp1) | reliable accuracy gains | wisdom of the crowd |
| Metacognitive sensitivity (Exp2) | no evidence | confidence modestly higher for correct decisions but did not reliably discriminate at trial level |
| Confidence-weighted fusion of pairs (Exp2) | reduced accuracy |  |
| Simulated AI guidance (Exp3-4) | helped when highly accurate; offset by following incorrect advice | reliance unaffected by deepfake prevalence |

## Moderators {#moderators}

- Guidance accuracy
- Aggregation method

## Limitations {#limitations}

- Values not checked against full text
- Simulated, not real, algorithmic guidance

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | OpenAlex abstract record for doi:10.1186/s41235-026-00758-2 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/plohl-2026-political-deepfakes-plausibility.md
================================================================================

---
type: source
id: plohl-2026-political-deepfakes-plausibility
doi: 10.1057/s41599-026-09122-7
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#plohl-2026-political-deepfakes-plausibility
---

# How deepfake plausibility, media literacy, and personal attitudes shape detection, liking, and intentions to share political deepfakes on social media

> **Source record** `plohl-2026-political-deepfakes-plausibility` · 2026 · Humanities and Social Sciences Communications  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** High-plausibility political deepfakes were detected less often and liked more; higher media literacy predicted better detection, less liking and lower sharing intention; favourable attitudes to the depicted person raised liking and sharing.

## Citation {#citation}

Nejc Plohl, Izidor Mlakar, Letizia Aquilino, Michele Brienza, Piercosma Bisconti, Urška Smrke (2026). How deepfake plausibility, media literacy, and personal attitudes shape detection, liking, and intentions to share political deepfakes on social media. *Humanities and Social Sciences Communications*. https://doi.org/10.1057/s41599-026-09122-7

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#plohl-2026-political-deepfakes-plausibility](/data/citations.json#plohl-2026-political-deepfakes-plausibility)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Online experiment in three countries; participants watched manipulated political videos (climate change, immigration) varying in plausibility (technical realism and fit with the speaker's known stance); outcomes: detection, liking, sharing intention |
| Modality | video, audiovisual |
| Stimuli | Political deepfake videos on climate change and immigration, high vs low plausibility |
| Participants (N) | 1124 |
| N detail | UK, Slovenia and Italy |
| Topics | human-detection, political-deepfakes, media-literacy, sharing |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Plausibility → detection | high-plausibility deepfakes less likely to be detected | no values in abstract |
| Plausibility → liking / sharing | more liking; no significant effect on sharing intention |  |
| Media literacy | better detection, less liking, lower sharing intention | strong predictor across outcomes |
| Attitude toward depicted person | more positive → more liking and sharing intention |  |
| Mediation | liking partially mediated detection → sharing |  |

## Moderators {#moderators}

- Plausibility (technical realism, stance congruence)
- Media literacy
- Attitude toward the speaker
- Topic

## Limitations {#limitations}

- Values not checked against full text
- Detection rates not reported in abstract

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | OpenAlex abstract record for doi:10.1057/s41599-026-09122-7 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/san-segundo-2026-audio-language-style.md
================================================================================

---
type: source
id: san-segundo-2026-audio-language-style
doi: 10.1016/j.specom.2026.103487
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#san-segundo-2026-audio-language-style
---

# Human perception of audio deepfakes: the role of language and speaking style

> **Source record** `san-segundo-2026-audio-language-style` · 2026 · Speech Communication  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Spanish and Japanese listeners averaged 59.11% when classifying natural vs synthetic speech, doing better on real voices, and relied mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter).

## Citation {#citation}

Eugenia San Segundo, Aurora López-Jareño, Xin Wang, Junichi Yamagishi (2026). Human perception of audio deepfakes: the role of language and speaking style. *Speech Communication*, 185, 103487. https://doi.org/10.1016/j.specom.2026.103487

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Perception experiment: listeners classified voices as natural or synthetic and justified each choice; stimuli varied by language (Spanish, Japanese), speaking style (audiobook, interview) and voice familiarity (familiar, unfamiliar) |
| Modality | audio |
| Stimuli | 80 stimuli, 50% artificial (synthesis system not in abstract) |
| Participants (N) | 54 |
| N detail | 28 native Spanish speakers, 26 Japanese speakers |
| Topics | human-detection, audio, cross-linguistic, cues |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Average accuracy | 59.11% | better on authentic samples |
| Cues used | mainly suprasegmental / extralinguistic | intonation, rhythm, fluency, pauses, speed, breathing, laughter rather than segmental features |
| Cross-linguistic | shared cues plus notable differences in how 'humanness' is conceptualised | Japanese vs Spanish listeners |

## Moderators {#moderators}

- Listener language
- Speaking style
- Voice familiarity

## Limitations {#limitations}

- Values not checked against full text
- Small N
- Condition-level accuracies not in abstract

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | OpenAlex abstract record for doi:10.1016/j.specom.2026.103487 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/manhede-2026-online-markets-training.md
================================================================================

---
type: source
id: manhede-2026-online-markets-training
doi: 10.1007/s00146-026-03373-3
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#manhede-2026-online-markets-training
---

# Human discernment of artificial intelligence in online markets can be shaped by experience and training

> **Source record** `manhede-2026-online-markets-training` · 2026 · AI & Society  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Baseline discernment of AI-generated vs human rental listings was 57.4%; heavier AI engagement predicted worse discernment (r = -0.29) while AI attitudes did not; labelled training with feedback added 9.4 points, control training none.

## Citation {#citation}

Tanaka Manhede, Yuliana Fartachuk, Steven A. Martinez, Allison Langer, Jason M. Chein (2026). Human discernment of artificial intelligence in online markets can be shaped by experience and training. *AI & Society*. https://doi.org/10.1007/s00146-026-03373-3

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#manhede-2026-online-markets-training](/data/citations.json#manhede-2026-online-markets-training)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Correlational baseline (AI attitudes, AI engagement vs discernment) plus a two-group training study: experimental group trained on labelled human/AI examples, then immediate feedback and per-correct monetary reward; control group did similar exercises with unlabelled examples and delayed, performance-based reward |
| Modality | text, image-general |
| Stimuli | Vacation-rental listings: human-written (Airbnb) or AI-generated (ChatGPT or Claude) descriptions; half text-only, half paired with property-exterior and host-face images |
| Participants (N) | 117 |
| N detail | 120 recruited, 3 excluded; experimental n=60, control n=57 |
| Topics | human-detection, ai-text, training, individual-differences |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Baseline accuracy (all) | 57.4% | t(116)=6.87, p<0.001; experimental 57.6%, control 57.3% |
| AI engagement (SCAIES) vs discernment | r(115) = -0.29, p = 0.002 | socioaffective engagement r = -0.26 |
| Attitudes toward AI vs discernment | r = -0.14, p = 0.14 | n.s. |
| Training gain, experimental | +9.4 points baseline → final | ≈6.7 after stage 1; further 2.7 n.s. (p = 0.085) |
| Change, control | -0.6 points (p = 0.74) |  |

## Moderators {#moderators}

- AI-platform engagement
- Labelled examples + immediate feedback

## Limitations {#limitations}

- Single listing genre
- Short-term; no retention test
- Logged ChatGPT use did not significantly correlate with discernment (r = -0.11)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-18 |
| Via | link.springer.com article page (open access, CC BY) |


================================================================================
URL: https://research.breacher.ai/sources/sulimani-2026-recognition-ai-faces.md
================================================================================

---
type: source
id: sulimani-2026-recognition-ai-faces
doi: 10.1177/03010066261442089
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#sulimani-2026-recognition-ai-faces
---

# Recognition of real and artificial intelligence-generated faces

> **Source record** `sulimani-2026-recognition-ai-faces` · 2026 · Perception  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** AI faces were recognised (remembered) more accurately than real faces, both face types were classified above chance with no difference, and recognition and classification accuracy were not correlated.

## Citation {#citation}

N. Sulimani, A. van Hunenstijn, A. Albonico (2026). Recognition of real and artificial intelligence-generated faces. *Perception*, 55(10), 924-937. https://doi.org/10.1177/03010066261442089

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#sulimani-2026-recognition-ai-faces](/data/citations.json#sulimani-2026-recognition-ai-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Face-memory (recognition) task with real and AI-generated faces, plus a human-vs-AI classification task; correlation between recognition and classification accuracy |
| Modality | image-face |
| Stimuli | Real and AI-generated faces (generator not in abstract) |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | face-recognition, memory, human-detection, covert-recognition |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Recognition memory | significantly higher for AI faces than real faces | no values in abstract |
| Classification | above chance for both face types; no difference between them |  |
| Recognition vs classification | not significantly correlated | authors suggest partial unawareness of the AI-face memory advantage |

## Limitations {#limitations}

- Values not checked against full text
- Generator and N not recorded

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | Europe PMC abstract record (PMID 42053250) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/yeung-2026-isthisai-strategies.md
================================================================================

---
type: source
id: yeung-2026-isthisai-strategies
doi: 10.1609/aies.v9i4.43351
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#yeung-2026-isthisai-strategies
---

# Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits

> **Source record** `yeung-2026-isthisai-strategies` · 2026 · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Catalogued 12 strategies people use in the wild to judge whether content is AI-generated (e.g., close inspection of physical details, recognising AI content trends, assumptions about model capabilities) and showed that strategies shift over time with AI capabilities and online trends.

## Citation {#citation}

Christina Yeung, Galen Weld, Jaron Mink, Franziska Roesner (2026). Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits. *Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)*, 9(4), 2800-2812. https://doi.org/10.1609/aies.v9i4.43351

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#yeung-2026-isthisai-strategies](/data/citations.json#yeung-2026-isthisai-strategies)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Mixed-methods longitudinal content analysis of crowd AI-detection discussions on r/isthisAI and r/RealOrAI over 2 years 8 months |
| Modality | image-general, video, text |
| Stimuli | 13,098 posts and 222,060 comments |
| Participants (N) | not recorded |
| N detail | observational; posts/comments rather than participants |
| Topics | detection-strategies, folk-heuristics, crowds |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Corpus | 13,098 posts; 222,060 comments | 2 years 8 months |
| Strategies catalogued | 12 | prevalence analysed; no percentages in abstract |
| Change over time | strategies and mental models shift with AI capabilities and social trends |  |

## Moderators {#moderators}

- Time / generator generation

## Limitations {#limitations}

- Does not measure accuracy of strategies
- Self-selected Reddit communities
- Values not checked against full text

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | ojs.aaai.org/index.php/AIES/article/view/43351 (abstract page) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/mori-2012-uncanny-valley.md
================================================================================

---
type: source
id: mori-2012-uncanny-valley
doi: 10.1109/MRA.2012.2192811
evidence_tier: C
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#mori-2012-uncanny-valley
---

# The uncanny valley [From the Field]

> **Source record** `mori-2012-uncanny-valley` · 2012 · IEEE Robotics & Automation Magazine  
> **Evidence tier:** C (essay) · **Verification:** unverified (compiler confidence: high)  
> **Bottom line:** Proposes that affinity rises with human likeness, drops sharply just short of full likeness, then recovers; movement amplifies the curve; designers should aim for the first peak.

## Citation {#citation}

Masahiro Mori, Karl F. MacDorman, Norri Kageki (2012). The uncanny valley [From the Field]. *IEEE Robotics & Automation Magazine*, 19(2), 98-100. https://doi.org/10.1109/MRA.2012.2192811

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#mori-2012-uncanny-valley](/data/citations.json#mori-2012-uncanny-valley)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Authorised English translation of Mori's 1970 essay 'Bukimi no tani' (Energy 7(4):33-35). Theoretical, non-empirical. |
| Modality | robot, prosthetic |
| Stimuli | Thought experiment (industrial robot, toy robot, prosthetic hand, bunraku puppet, corpse, zombie) |
| Participants (N) | not recorded |
| N detail | non-empirical |
| Topics | uncanny-valley, theory |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Hypothesised curve | non-monotonic (valley near high human likeness) | y-axis 'shinwakan' variously translated as affinity/familiarity |

## Moderators {#moderators}

- Movement deepens the valley (hypothesised)

## Mechanism relevance {#mechanism}

`human-likeness`

## Limitations {#limitations}

- Not empirically derived

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | high |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/mathur-reichling-2016-cartography.md
================================================================================

---
type: source
id: mathur-reichling-2016-cartography
doi: 10.1016/j.cognition.2015.09.008
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#mathur-reichling-2016-cartography
---

# Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley

> **Source record** `mathur-reichling-2016-cartography` · 2016 · Cognition  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Demonstrated an uncanny valley in explicit likability of real robot faces that extended to implicit trust decisions; category confusion may occur but did not mediate the effect.

## Citation {#citation}

Maya B. Mathur, David B. Reichling (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. *Cognition*, 146, 22-32. https://doi.org/10.1016/j.cognition.2015.09.008

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#mathur-reichling-2016-cartography](/data/citations.json#mathur-reichling-2016-cartography)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Ratings of 80 objectively sampled real-world robot faces plus a controlled set of edited faces; explicit likability; implicit trust via an investment game |
| Modality | robot-face |
| Stimuli | 80 real robot faces; digitally edited face series |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | uncanny-valley, trust, valley-shape |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Explicit likability | uncanny valley present |  |
| Implicit trust (investment game) | valley extends to trust decisions | driven by subtle facial-expression cues also used for humans |
| Category confusion | may occur; does not mediate likability effect | preliminary |

## Mechanism relevance {#mechanism}

`against-categorization-ambiguity`

## Limitations {#limitations}

- Static images
- Robot faces, not synthetic human faces

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/mathur-2020-uncanny-not-confusing.md
================================================================================

---
type: source
id: mathur-2020-uncanny-not-confusing
doi: 10.1016/j.chb.2019.08.029
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#mathur-2020-uncanny-not-confusing
---

# Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley

> **Source record** `mathur-2020-uncanny-not-confusing` · 2020 · Computers in Human Behavior  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: medium)  
> **Bottom line:** The likability valley replicated across sites; perceptual category confusion again did not mediate it.

## Citation {#citation}

Maya B. Mathur, David B. Reichling, Francesca Lunardini, et al. (2020). Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley. *Computers in Human Behavior*, 103, 21-30. https://doi.org/10.1016/j.chb.2019.08.029

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#mathur-2020-uncanny-not-confusing](/data/citations.json#mathur-2020-uncanny-not-confusing)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Multisite replication and extension of Mathur & Reichling (2016) |
| Modality | robot-face |
| Stimuli | Robot faces |
| Participants (N) | not recorded |
| N detail | multisite (not recorded) |
| Topics | uncanny-valley, replication, valley-shape |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Valley replication | replicated |  |
| Category confusion mediation | not supported |  |

## Mechanism relevance {#mechanism}

`against-categorization-ambiguity`

## Limitations {#limitations}

- Robot faces, not synthetic human faces

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | medium |
| Checked on | — |
| Via | compiler knowledge; DOI not resolved |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/katsyri-2015-uv-review.md
================================================================================

---
type: source
id: katsyri-2015-uv-review
doi: 10.3389/fpsyg.2015.00390
evidence_tier: C
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#katsyri-2015-uv-review
---

# A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness

> **Source record** `katsyri-2015-uv-review` · 2015 · Frontiers in Psychology  
> **Evidence tier:** C (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** The simplest valley hypothesis and the movement claim lacked consistent support; categorization ambiguity had too little research to judge; perceptual mismatch received good support; the valley appears only under specific conditions.

## Citation {#citation}

Jari Kätsyri, Klaus Förger, Meeri Mäkäräinen, Tapio Takala (2015). A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness. *Frontiers in Psychology*, 6, 390. https://doi.org/10.3389/fpsyg.2015.00390

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#katsyri-2015-uv-review](/data/citations.json#katsyri-2015-uv-review)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Narrative review of empirical tests of uncanny valley hypotheses |
| Modality | review |
| Stimuli | n/a |
| Participants (N) | not recorded |
| N detail | review |
| Topics | uncanny-valley, mechanisms, review |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Simple human-likeness valley | no consistent support |  |
| Movement modulates valley | no consistent support |  |
| Categorization ambiguity | insufficient research |  |
| Perceptual mismatch | good support |  |

## Mechanism relevance {#mechanism}

`perceptual-mismatch`

## Limitations {#limitations}

- Narrative, not quantitative

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/diel-2022-uv-meta.md
================================================================================

---
type: source
id: diel-2022-uv-meta
doi: 10.1145/3470742
evidence_tier: A
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#diel-2022-uv-meta
---

# A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

> **Source record** `diel-2022-uv-meta` · 2022 · ACM Transactions on Human-Robot Interaction  
> **Evidence tier:** A (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Across 72 studies the uncanny valley effect was large (Hedges' g = 1.01); face distortion produced the largest effect (g = 1.46), followed by distinct entities, realism rendering and morphing.

## Citation {#citation}

Alexander Diel, Sarah Weigelt, Karl F. MacDorman (2022). A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables. *ACM Transactions on Human-Robot Interaction*, 11(1), Article 1. https://doi.org/10.1145/3470742

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#diel-2022-uv-meta](/data/citations.json#diel-2022-uv-meta)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Meta-analysis: 72 of 468 screened studies; 247 effect sizes; moderator analysis by stimulus-creation technique and measure |
| Modality | robot, cg-face, android |
| Stimuli | 10 stimulus-creation techniques; 39 affect measures; 14 indirect measures |
| Participants (N) | not recorded |
| N detail | 72 studies, 247 effect sizes; published online 2021 |
| Topics | uncanny-valley, meta-analysis, mechanisms, measurement |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Overall effect | Hedges' g = 1.01 [0.80, 1.22] | 247 effect sizes |
| Face distortion | g = 1.46 [0.69, 2.24] | largest |
| Distinct entities | g = 1.20 [1.02, 1.38] |  |
| Realism render | g = 0.99 [0.62, 1.36] |  |
| Morphing | g = 0.94 [0.64, 1.24] |  |
| Largest-effect affect measures | threatening, likable, aesthetics, familiarity, eeriness |  |
| Largest-effect indirect measures | dislike frequency, categorization RT, like frequency, avoidance, viewing duration |  |

## Moderators {#moderators}

- Stimulus-creation technique; outcome measure

## Mechanism relevance {#mechanism}

`atypicality`, `perceptual-mismatch`

## Limitations {#limitations}

- No consensus on theoretical basis or methodology (authors)
- Heterogeneous stimuli

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/macdorman-chattopadhyay-2016.md
================================================================================

---
type: source
id: macdorman-chattopadhyay-2016
doi: 10.1016/j.cognition.2015.09.019
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#macdorman-chattopadhyay-2016
---

# Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not

> **Source record** `macdorman-chattopadhyay-2016` · 2016 · Cognition  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Reducing realism consistency made humans and animals (not objects) eerier and colder; the most category-ambiguous stimuli were neither the eeriest nor the coldest, contradicting the category-uncertainty account.

## Citation {#citation}

Karl F. MacDorman, Debaleena Chattopadhyay (2016). Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. *Cognition*, 146, 190-205. https://doi.org/10.1016/j.cognition.2015.09.019

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#macdorman-chattopadhyay-2016](/data/citations.json#macdorman-chattopadhyay-2016)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Three rounds of experiments; categorisation and ratings of humans, animals and objects from computer-animated to real; realism consistency reduced by manipulating feature sets (for humans: eyes-eyelashes-mouth vs skin-nose-eyebrows) |
| Modality | cg-face |
| Stimuli | Human, animal and object continua from computer-animated to real |
| Participants (N) | 548 |
| N detail | across three rounds |
| Topics | uncanny-valley, mechanisms |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Realism inconsistency → eeriness/coldness | increase for humans and animals; not objects |  |
| Max category uncertainty → eeriness | no peak | Jentsch (1906) predictions not supported |

## Mechanism relevance {#mechanism}

`perceptual-mismatch`, `against-categorization-ambiguity`

## Limitations {#limitations}

- CG stimuli
- Online samples

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/seyama-nagayama-2007.md
================================================================================

---
type: source
id: seyama-nagayama-2007
doi: 10.1162/pres.16.4.337
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#seyama-nagayama-2007
---

# The uncanny valley: Effect of realism on the impression of artificial human faces

> **Source record** `seyama-nagayama-2007` · 2007 · Presence: Teleoperators and Virtual Environments  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: high)  
> **Bottom line:** Realism alone did not produce unpleasantness; strong unpleasantness appeared only when highly realistic faces had abnormal eye size.

## Citation {#citation}

Jun'ichiro Seyama, Ruth S. Nagayama (2007). The uncanny valley: Effect of realism on the impression of artificial human faces. *Presence: Teleoperators and Virtual Environments*, 16(4), 337-351. https://doi.org/10.1162/pres.16.4.337

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#seyama-nagayama-2007](/data/citations.json#seyama-nagayama-2007)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Morphs between real and artificial (doll/mask) faces with eye-size manipulation; pleasantness ratings |
| Modality | image-face |
| Stimuli | Real-to-artificial morph continua |
| Participants (N) | not recorded |
| N detail | small (not recorded) |
| Topics | uncanny-valley, mechanisms, atypicality |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Realism alone | no valley |  |
| Abnormal features × high realism | strong unpleasantness | realism amplifies sensitivity to deviations |

## Mechanism relevance {#mechanism}

`atypicality`, `perceptual-mismatch`

## Limitations {#limitations}

- Small samples
- Static images

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | high |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/cheetham-2014-happy-valley.md
================================================================================

---
type: source
id: cheetham-2014-happy-valley
doi: 10.3389/fpsyg.2014.01219
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#cheetham-2014-happy-valley
---

# Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'

> **Source record** `cheetham-2014-happy-valley` · 2014 · Frontiers in Psychology  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Contrary to the uncanny valley hypothesis, category-ambiguous faces were easier (not harder) to discriminate, and greater discrimination difficulty went with more positive affect — a 'Happy Valley'.

## Citation {#citation}

Marcus Cheetham, Pascal Suter, Lutz Jäncke (2014). Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'. *Frontiers in Psychology*, 5, 1219. https://doi.org/10.3389/fpsyg.2014.01219

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#cheetham-2014-happy-valley](/data/citations.json#cheetham-2014-happy-valley)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Three experiments on avatar-to-human morph continua: discrimination difficulty, affect, processing-bias control |
| Modality | cg-face |
| Stimuli | Avatar-human morph continua (static faces) |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | uncanny-valley, categorical-perception |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Discrimination difficulty | lower for ambiguous faces, higher for human faces | opposite of the profile UVH assumes |
| Difficulty ↔ affect | more difficulty → more positive affect | strongest for ambiguous faces |
| Processing level | avatars at category level, humans at individual level |  |

## Mechanism relevance {#mechanism}

`against-categorization-ambiguity`

## Limitations {#limitations}

- Static morph stimuli

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/burleigh-2013-does-uv-exist.md
================================================================================

---
type: source
id: burleigh-2013-does-uv-exist
doi: 10.1016/j.chb.2012.11.021
evidence_tier: B
verification: verified-doi
citations_entry: https://research.breacher.ai/data/citations.json#burleigh-2013-does-uv-exist
---

# Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces

> **Source record** `burleigh-2013-does-uv-exist` · 2013 · Computers in Human Behavior  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-doi (compiler confidence: medium)  
> **Bottom line:** Eeriness was mostly a linear function of human likeness; non-linear effects appeared only when stimuli combined conflicting or atypical features.

## Citation {#citation}

Tyler J. Burleigh, Jordan R. Schoenherr, Guy L. Lacroix (2013). Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces. *Computers in Human Behavior*, 29(3), 759-771. https://doi.org/10.1016/j.chb.2012.11.021

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#burleigh-2013-does-uv-exist](/data/citations.json#burleigh-2013-does-uv-exist)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Two studies varying human likeness of computer-generated faces; eeriness ratings |
| Modality | cg-face |
| Stimuli | Digitally created faces |
| Participants (N) | not recorded |
| N detail | modest (not recorded) |
| Topics | uncanny-valley, valley-shape |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Eeriness vs human likeness | mostly linear | no valley along a single axis |
| Conflicting/atypical features | non-linear effects |  |

## Mechanism relevance {#mechanism}

`atypicality`, `perceptual-mismatch`

## Limitations {#limitations}

- CG stimuli only

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-doi` |
| Compiler confidence | medium |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar metadata (title, authors, venue, DOI); abstract not available |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/steckenfinger-ghazanfar-2009.md
================================================================================

---
type: source
id: steckenfinger-ghazanfar-2009
doi: 10.1073/pnas.0910063106
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#steckenfinger-ghazanfar-2009
---

# Monkey visual behavior falls into the uncanny valley

> **Source record** `steckenfinger-ghazanfar-2009` · 2009 · Proceedings of the National Academy of Sciences (PNAS)  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: high)  
> **Bottom line:** Macaques looked less at realistic synthetic faces than at real or unrealistic ones—an uncanny-valley analogue in another species.

## Citation {#citation}

Shawn A. Steckenfinger, Asif A. Ghazanfar (2009). Monkey visual behavior falls into the uncanny valley. *Proceedings of the National Academy of Sciences (PNAS)*, 106(43), 18362-18366. https://doi.org/10.1073/pnas.0910063106

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#steckenfinger-ghazanfar-2009](/data/citations.json#steckenfinger-ghazanfar-2009)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Looking-time preference in macaque monkeys for real, realistic-synthetic, and unrealistic-synthetic monkey faces |
| Modality | image-face, non-human-primate |
| Stimuli | Real monkey faces; realistic and unrealistic synthetic monkey faces |
| Participants (N) | 5 |
| N detail | macaques (approximate) |
| Topics | uncanny-valley, evolution |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Looking time | realistic-synthetic < real and < unrealistic |  |

## Mechanism relevance {#mechanism}

`evolved-perceptual-basis`

## Limitations {#limitations}

- Very small N
- Looking time is an indirect aversion proxy

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | high |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/wang-2015-uv-existence-explanations.md
================================================================================

---
type: source
id: wang-2015-uv-existence-explanations
doi: 10.1037/gpr0000056
evidence_tier: C
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#wang-2015-uv-existence-explanations
---

# The uncanny valley: Existence and explanations

> **Source record** `wang-2015-uv-existence-explanations` · 2015 · Review of General Psychology  
> **Evidence tier:** C (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: high)  
> **Bottom line:** Proposes that humanlike agents are first processed as human; detecting non-human cues then triggers dehumanization, which produces eeriness.

## Citation {#citation}

Shensheng Wang, Scott O. Lilienfeld, Philippe Rochat (2015). The uncanny valley: Existence and explanations. *Review of General Psychology*, 19(4), 393-407. https://doi.org/10.1037/gpr0000056

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#wang-2015-uv-existence-explanations](/data/citations.json#wang-2015-uv-existence-explanations)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Theoretical review; proposes the dehumanization hypothesis |
| Modality | review |
| Stimuli | n/a |
| Participants (N) | not recorded |
| N detail | review |
| Topics | uncanny-valley, mechanisms, theory |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Dehumanization hypothesis | proposed | few direct tests |

## Mechanism relevance {#mechanism}

`dehumanization`

## Limitations {#limitations}

- Theory, not data

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | high |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/gray-wegner-2012-mind-perception.md
================================================================================

---
type: source
id: gray-wegner-2012-mind-perception
doi: 10.1016/j.cognition.2012.06.007
evidence_tier: B
verification: verified-doi
citations_entry: https://research.breacher.ai/data/citations.json#gray-wegner-2012-mind-perception
---

# Feeling robots and human zombies: Mind perception and the uncanny valley

> **Source record** `gray-wegner-2012-mind-perception` · 2012 · Cognition  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-doi (compiler confidence: high)  
> **Bottom line:** Unease was driven by attributing the capacity to feel (experience) to machines—or denying it to humans—not by appearance alone.

## Citation {#citation}

Kurt Gray, Daniel M. Wegner (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. *Cognition*, 125(1), 125-130. https://doi.org/10.1016/j.cognition.2012.06.007

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#gray-wegner-2012-mind-perception](/data/citations.json#gray-wegner-2012-mind-perception)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 3 experiments: humanlike vs mechanical view of the same robot; computer described as able to feel vs act; human described as lacking experience |
| Modality | robot, vignette |
| Stimuli | KASPAR robot video (front vs back); vignettes |
| Participants (N) | not recorded |
| N detail | 3 modest samples (not recorded) |
| Topics | uncanny-valley, mind-perception |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Humanlike robot unease | higher; mediated by perceived experience |  |
| Machine that can feel vs act | feeling machine more unnerving |  |
| Human lacking experience | unnerving |  |

## Mechanism relevance {#mechanism}

`mind-perception`

## Limitations {#limitations}

- Small samples
- Vignette paradigms

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-doi` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | OpenAlex metadata (title, authors, venue, volume, pages, DOI); abstract not available; findings from compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/stein-ohler-2017-uncanny-mind.md
================================================================================

---
type: source
id: stein-ohler-2017-uncanny-mind
doi: 10.1016/j.cognition.2016.12.010
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#stein-ohler-2017-uncanny-mind
---

# Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting

> **Source record** `stein-ohler-2017-uncanny-mind` · 2017 · Cognition  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: high)  
> **Bottom line:** With visuals held constant, eeriness was highest when empathic behaviour was attributed to an autonomous AI—an 'uncanny valley of mind'.

## Citation {#citation}

Jan-Philipp Stein, Peter Ohler (2017). Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. *Cognition*, 160, 43-50. https://doi.org/10.1016/j.cognition.2016.12.010

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#stein-ohler-2017-uncanny-mind](/data/citations.json#stein-ohler-2017-uncanny-mind)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 2×2 between-subjects VR study: identical characters having an empathic conversation, framed as human- vs AI-controlled and scripted vs autonomous |
| Modality | vr-avatar |
| Stimuli | Identical VR characters; framing manipulated |
| Participants (N) | 92 |
| Topics | uncanny-valley, mind-perception |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Eeriness peak condition | autonomous AI + empathic behaviour | appearance held constant |

## Mechanism relevance {#mechanism}

`mind-perception`

## Limitations {#limitations}

- Single scenario
- Moderate N

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | high |
| Checked on | — |
| Via | compiler knowledge |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/saygin-2012-predictive-coding.md
================================================================================

---
type: source
id: saygin-2012-predictive-coding
doi: 10.1093/scan/nsr025
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#saygin-2012-predictive-coding
---

# The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions

> **Source record** `saygin-2012-predictive-coding` · 2012 · Social Cognitive and Affective Neuroscience  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Repetition suppression in bilateral anterior intraparietal sulcus was stronger for the android (appearance–motion mismatch) than for the human or robot, interpreted as prediction error; authors state this cannot establish a causal link to the uncanny valley.

## Citation {#citation}

Ayse Pinar Saygin, Thierry Chaminade, Hiroshi Ishiguro, Jon Driver, Chris Frith (2012). The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions. *Social Cognitive and Affective Neuroscience*, 7(4), 413-422. https://doi.org/10.1093/scan/nsr025

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#saygin-2012-predictive-coding](/data/citations.json#saygin-2012-predictive-coding)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Event-related fMRI repetition suppression (3T); 2-s grayscale clips of 8 actions by a human, the Repliee Q2 android (human appearance, mechanical motion) and the same robot with skin removed (mechanical appearance and motion) |
| Modality | video, robot |
| Stimuli | Action videos: Human, Android, Robot |
| Participants (N) | 19 |
| N detail | 20 recruited; 1 excluded for head motion |
| Topics | uncanny-valley, neural, predictive-coding |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| aIPS repetition suppression | Android > Human and Robot (bilateral) | key action-perception node |
| Left extrastriate body area | Human and Android > Robot | sensitive to humanlike form |
| Selectivity for appearance or motion alone | little (outside EBA) | network responded to the match between them |

## Mechanism relevance {#mechanism}

`perceptual-mismatch`, `predictive-coding`

## Limitations {#limitations}

- N=19
- Single android
- Authors: no conclusive or causal link to the uncanny valley

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | academic.oup.com article page |


================================================================================
URL: https://research.breacher.ai/sources/rosenthal-von-der-putten-2019-vmpfc.md
================================================================================

---
type: source
id: rosenthal-von-der-putten-2019-vmpfc
doi: 10.1523/JNEUROSCI.2956-18.2019
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#rosenthal-von-der-putten-2019-vmpfc
---

# Neural mechanisms for accepting and rejecting artificial social partners in the uncanny valley

> **Source record** `rosenthal-von-der-putten-2019-vmpfc` · 2019 · Journal of Neuroscience  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** vmPFC encoded likability as a non-linear function of humanlikeness with selectively low likability for highly humanlike agents; behavioural valley in 18 of 21 participants; DMPFC encoded choices about artificial agents and an amygdala signal predicted their rejection.

## Citation {#citation}

Astrid M. Rosenthal-von der Pütten, Nicole C. Krämer, Stefan Maderwald, Matthias Brand, Fabian Grabenhorst (2019). Neural mechanisms for accepting and rejecting artificial social partners in the uncanny valley. *Journal of Neuroscience*, 39(33), 6555-6570. https://doi.org/10.1523/JNEUROSCI.2956-18.2019

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#rosenthal-von-der-putten-2019-vmpfc](/data/citations.json#rosenthal-von-der-putten-2019-vmpfc)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 7T fMRI over six sessions; rating task (72 trials: likability, familiarity, humanlikeness) and choice task (108 trials: choose which of two agents receives a personal gift) |
| Modality | image, robot, android |
| Stimuli | Six categories: humans, humans with impairments, artificial humans, androids, humanoid robots, mechanoid robots |
| Participants (N) | 21 |
| N detail | 26 recruited; 4 excluded |
| Topics | uncanny-valley, neural, decision-making |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Behavioural valley (artificial humans below linear trend) | significant in 18 of 21 individuals |  |
| vmPFC | non-linear likability (valley) signal | approximated by multiplicative combination of TPJ, DMPFC, fusiform signals |
| TPJ | linear humanlikeness code |  |
| DMPFC, fusiform | non-linear human/non-human distinction |  |
| Choice decision variable | vmPFC for human choices; DMPFC for artificial-agent choices |  |
| Amygdala | predicted rejection of artificial agents |  |

## Mechanism relevance {#mechanism}

`valuation`

## Limitations {#limitations}

- N=21
- Static images

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | jneurosci.org article page |


================================================================================
URL: https://research.breacher.ai/sources/schindler-2017-erp-realism.md
================================================================================

---
type: source
id: schindler-2017-erp-realism
doi: 10.1038/srep45003
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#schindler-2017-erp-realism
---

# Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory

> **Source record** `schindler-2017-erp-realism` · 2017 · Scientific Reports  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** N170 was U-shaped across realism (strongest for most abstract and most realistic faces), LPP increased linearly with realism, and EPN was unaffected by realism; no uncanny 'dip' was directly observed.

## Citation {#citation}

Sebastian Schindler, Eduard Zell, Mario Botsch, Johanna Kissler (2017). Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory. *Scientific Reports*, 7, 45003. https://doi.org/10.1038/srep45003

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#schindler-2017-erp-realism](/data/citations.json#schindler-2017-erp-realism)

## Study design {#design}

| Field | Value |
|---|---|
| Design | EEG/ERP; the same faces rendered at 6 realism levels (1 = abstract to 6 = real photograph) × happy/angry/neutral expressions; no eeriness or likability ratings |
| Modality | cg-face |
| Stimuli | Stylisation continuum from abstract to photographic |
| Participants (N) | 33 |
| N detail | recruited (22 female, mean age 23.3); analysed N not confirmed |
| Topics | uncanny-valley, neural |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| N170 | U-shaped (quadratic) across realism | stronger for most abstract and most realistic; realism × emotion interaction |
| EPN | no realism effect | modulated by emotion only (largest for angry) |
| LPP | linear increase with realism | no emotion effect |
| Uncanny valley in ERPs | not directly observed | authors suggest a perceptual basis for the hypothesis |

## Moderators {#moderators}

- Happy expressions elicited weaker N170 as realism increased; angry expressions stronger

## Mechanism relevance {#mechanism}

`perceptual-basis`, `against-affective-valley`

## Limitations {#limitations}

- No eeriness/likability measure
- Stylised CG continuum, not synthetic photoreal faces

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | nature.com article page (open access); author list on page may be truncated |


================================================================================
URL: https://research.breacher.ai/sources/moshel-2022-decoding-ai-faces.md
================================================================================

---
type: source
id: moshel-2022-decoding-ai-faces
doi: 10.1016/j.visres.2022.108079
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#moshel-2022-decoding-ai-faces
---

# Are you for real? Decoding realistic AI-generated faces from neural activity

> **Source record** `moshel-2022-decoding-ai-faces` · 2022 · Vision Research  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Group-level behavioural judgments were near chance (people often called real faces fake and vice versa), yet neural activity reliably distinguished AI-generated from real faces.

## Citation {#citation}

Michoel L. Moshel, Amanda K. Robinson, Thomas A. Carlson, Tijl Grootswagers (2022). Are you for real? Decoding realistic AI-generated faces from neural activity. *Vision Research*, 199, 108079. https://doi.org/10.1016/j.visres.2022.108079

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#moshel-2022-decoding-ai-faces](/data/citations.json#moshel-2022-decoding-ai-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Behavioural real-vs-AI classification plus EEG multivariate decoding of real vs AI faces |
| Modality | image-face |
| Stimuli | Realistic GAN faces vs real faces |
| Participants (N) | not recorded |
| N detail | not in abstract |
| Topics | uncanny-valley, neural, implicit-detection, human-detection |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Behavioural real-vs-AI judgments | near chance at group level | frequent label swaps; ≈37% figure in press coverage not checked |
| EEG decoding of real vs AI | reliable (above chance) | ≈54% figure in press coverage not checked |

## Mechanism relevance {#mechanism}

`implicit-sensitivity`

## Limitations {#limitations}

- Decoding ≠ conscious or affective response
- Sample sizes not checked

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/eiserbeck-2023-deepfake-smiles.md
================================================================================

---
type: source
id: eiserbeck-2023-deepfake-smiles
doi: 10.1038/s41598-023-42802-x
evidence_tier: B
verification: verified-fulltext
citations_entry: https://research.breacher.ai/data/citations.json#eiserbeck-2023-deepfake-smiles
---

# Deepfake smiles matter less—the psychological and neural impact of presumed AI-generated faces

> **Source record** `eiserbeck-2023-deepfake-smiles` · 2023 · Scientific Reports  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Simply labelling a real smiling face as 'fake' made it rated less positive and removed its early neural smile effects (P1/N170/EPN), while increasing late, effortful processing (LPP); angry faces were unaffected by the label.

## Citation {#citation}

Anna Eiserbeck, Martin Maier, Julia Baum, Rasha Abdel Rahman (2023). Deepfake smiles matter less—the psychological and neural impact of presumed AI-generated faces. *Scientific Reports*, 13, 16111. https://doi.org/10.1038/s41598-023-42802-x

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#eiserbeck-2023-deepfake-smiles](/data/citations.json#eiserbeck-2023-deepfake-smiles)

## Study design {#design}

| Field | Value |
|---|---|
| Design | EEG (64 ch) belief-manipulation study: real face photographs labelled on screen as 'REAL' or 'FAKE' (cover story), valence rating on 100-point scale; label and emotion counterbalanced |
| Modality | image-face |
| Stimuli | 540 real photographs: 180 identities × angry/neutral/happy (RaFD, KDEF, FACES databases); no image was actually synthetic |
| Participants (N) | 30 |
| N detail | 21 female, 9 male; mean age 25.9; 7 datasets excluded and replaced |
| Topics | uncanny-valley, neural, labelling, emotion |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Valence rating, happy faces | fake 73.12 vs real 74.97 (0-100) | label effect only for happy faces |
| Rating RT, happy faces | ≈1,366 ms fake vs ≈1,290 ms real | slower for presumed fakes |
| Overall valence | fake 49.80 vs real 50.78 | small |
| P1 / N170 / EPN smile effect (happy vs neutral) | present for real-labelled only | EPN for fake p=.066 |
| LPP smile effect | present for fake-labelled only | more positive LPP → more effortful evaluation |
| Angry vs neutral | N170, EPN, LPP effects for both labels | negative expressions unaffected by label |

## Mechanism relevance {#mechanism}

`top-down-belief`, `dehumanization`

## Limitations {#limitations}

- Label × emotion interaction significant behaviourally but not in ERPs (authors' caveat)
- Small N
- Belief manipulation only; no actual synthetic faces

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | nature.com article page (open access); author list on page truncated |


================================================================================
URL: https://research.breacher.ai/sources/weisman-pena-2021-doppelganger.md
================================================================================

---
type: source
id: weisman-pena-2021-doppelganger
doi: null
evidence_tier: B
verification: unverified
citations_entry: https://research.breacher.ai/data/citations.json#weisman-pena-2021-doppelganger
---

# Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions

> **Source record** `weisman-pena-2021-doppelganger` · 2021 · Cyberpsychology, Behavior, and Social Networking  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** unverified (compiler confidence: medium)  
> **Bottom line:** Self-resembling deepfake avatars increased uncanniness, which in turn reduced affect-based trust.

## Citation {#citation}

William D. Weisman, Jorge F. Peña (2021). Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions. *Cyberpsychology, Behavior, and Social Networking*, 24(3), 182-187.

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#weisman-pena-2021-doppelganger](/data/citations.json#weisman-pena-2021-doppelganger)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Experiment with self-resembling deepfake talking-head avatars; uncanniness and affect-based trust measures |
| Modality | video |
| Stimuli | Participant-doppelganger deepfake talking heads |
| Participants (N) | not recorded |
| N detail | not recorded |
| Topics | uncanny-valley, deepfake-video, trust |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Doppelganger → uncanniness | increase |  |
| Uncanniness → affect-based trust | decrease (mediation) |  |

## Mechanism relevance {#mechanism}

`uncanny-to-trust`

## Limitations {#limitations}

- Details pending verification
- 2020-era deepfake quality

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `unverified` |
| Compiler confidence | medium |
| Checked on | — |
| Via | compiler knowledge; DOI unknown |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/lee-2026-ai-chest-radiographs.md
================================================================================

---
type: source
id: lee-2026-ai-chest-radiographs
doi: 10.1007/s10278-026-02339-7
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#lee-2026-ai-chest-radiographs
---

# Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study

> **Source record** `lee-2026-ai-chest-radiographs` · 2026 · Journal of Imaging Informatics in Medicine  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Expert and non-expert physicians and frontier LLMs could not reliably tell AI-generated chest X-rays from real ones: the best human (radiologist, 75.8% balanced accuracy) and best LLM (77.9%) were similar but made different errors, radiologists called 38-45% of real images fake, and a strong frequency-domain detector collapsed on a held-out generator (12% sensitivity).

## Citation {#citation}

Suji Lee, Jei Youn Park, Yongsik Sim, Youho Myong (2026). Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study. *Journal of Imaging Informatics in Medicine*. https://doi.org/10.1007/s10278-026-02339-7

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#lee-2026-ai-chest-radiographs](/data/citations.json#lee-2026-ai-chest-radiographs)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Reader study: 2 radiologists, 2 nonradiologist physicians and 3 frontier LLMs (GPT-5.5, Gemini 3.5 Flash, Claude Opus 4.8) classified chest radiographs as real or AI-generated; LLMs re-run 3 months later and with laterality markers/borders masked; frequency-domain baseline classifier for comparison |
| Modality | image-general |
| Stimuli | 160 posteroanterior chest radiographs: 40 real (NIH ChestX-ray14) and 120 synthetic (diffusion and text/LLM-based generation pipelines); 75% synthetic prevalence |
| Participants (N) | 4 |
| N detail | 4 human readers (2 radiologists, 2 nonradiologist physicians) plus 3 LLM evaluators |
| Topics | human-detection, human-vs-machine, expertise |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Top evaluator (Gemini 3.5 Flash) balanced accuracy | 77.9% | specificity 100%, sensitivity 55.8% |
| Best human reader (radiologist RAD1) balanced accuracy | 75.8% | sensitivity 55.0%, specificity 96.7%; paired difference −2.1 points [95% CI −5.8, 1.2] |
| Human readers: diffusion-generated images detected | 82-100% | all four human readers |
| Radiologists: real images misclassified as synthetic | 38-45% |  |
| Nonradiologist physicians | near chance |  |
| LLMs: LLM-generated images detected | 32-72% |  |
| LLM specificity with markers/borders masked | Gemini 62.5%; GPT-5.5 85.0% | vs 90-100% in same-day unmasked runs |
| Agreement among LLMs (Fleiss' kappa) | 0.213 | low interevaluator agreement |
| Frequency-domain baseline | balanced accuracy 98.3% in-distribution; 12% sensitivity on a held-out generator |  |

## Moderators {#moderators}

- Generator type: diffusion images were caught by humans far more often than LLM/text-pipeline images were caught by LLMs.
- Expertise: radiologists outperformed nonradiologists but at the cost of many false alarms on real images.

## Limitations {#limitations}

- Four human readers
- Medical images only
- Values from abstract; full text not read
- Fixed 75% synthetic prevalence

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7221727670 for doi:10.1007/s10278-026-02339-7 (publisher page could not be fetched) |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/chen-2026-humans-ai-deepfake-teaming.md
================================================================================

---
type: source
id: chen-2026-humans-ai-deepfake-teaming
doi: 10.1177/10711813261487968
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#chen-2026-humans-ai-deepfake-teaming
---

# How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming

> **Source record** `chen-2026-humans-ai-deepfake-teaming` · 2026 · Proceedings of the Human Factors and Ergonomics Society Annual Meeting  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Humans and AI detectors relied on different cues; eye-tracking and EEG suggested holistic, flexible human viewing that catches some inconsistencies AI misses, and people with more AI exposure matched or exceeded AI detectors in some contexts (abstract; no numbers reported).

## Citation {#citation}

Xinyi Chen, et al. (2026). How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming. *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*. https://doi.org/10.1177/10711813261487968

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#chen-2026-humans-ai-deepfake-teaming](/data/citations.json#chen-2026-humans-ai-deepfake-teaming)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Eye-tracking and EEG comparison of human and AI attention allocation in deepfake detection, across levels of AI literacy |
| Modality | video |
| Stimuli | Deepfake media manipulating lip movements, facial expressions and voices (details not in abstract) |
| Participants (N) | not recorded |
| N detail | N not reported in the abstract; OpenAlex lists Jing Yang (University at Buffalo) as last author; full author list not confirmed |
| Topics | human-detection, human-ai-teaming, neural |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Cue use | humans and AI rely on different cues and reasoning strategies | qualitative |
| Human vs AI detector | people with more AI exposure may match or exceed AI detectors in some contexts | no figures in abstract |
| Processing style | holistic, flexible visual processing | eye-tracking and EEG evidence |

## Moderators {#moderators}

- AI literacy / AI exposure

## Limitations {#limitations}

- Abstract reports no N or effect sizes
- Short proceedings paper
- Full text not read (closed access)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/naseem-2026-in-the-wild-detection-review.md
================================================================================

---
type: source
id: naseem-2026-in-the-wild-detection-review
doi: 10.1016/j.cosrev.2026.101081
evidence_tier: C
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#naseem-2026-in-the-wild-detection-review
---

# From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection

> **Source record** `naseem-2026-in-the-wild-detection-review` · 2026 · Computer Science Review  
> **Evidence tier:** C (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Review concluding that strong benchmark performance of deepfake detectors does not necessarily carry over to real-world media, because of platform compression, reposting, post-processing, modality mismatch, temporal drift and new generators; calls for deployment-oriented, continually re-benchmarked detection.

## Citation {#citation}

Muhammad Tahir Naseem, et al. (2026). From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection. *Computer Science Review*, 63, 101081. https://doi.org/10.1016/j.cosrev.2026.101081

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#naseem-2026-in-the-wild-detection-review](/data/citations.json#naseem-2026-in-the-wild-detection-review)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Narrative review of deepfake detection from the perspective of in-the-wild robustness, multimodal reasoning and cross-domain generalization (no systematic protocol described in abstract) |
| Modality | review |
| Stimuli | n/a (review) |
| Participants (N) | not recorded |
| N detail | Review; OpenAlex lists Wajid Zaman (Yeungnam University) as last author; full author list not confirmed |
| Topics | detector-generalization, review |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Benchmark-to-wild transfer | curated-benchmark performance does not necessarily translate into reliable real-world performance | qualitative |
| Causes of detector failure | generator shift, platform-induced degradation, modality mismatch, language and speaker variation, contextual diversity |  |
| Recommendations | continually updated benchmarks, missing-modality robustness, uncertainty-aware detection, provenance integration |  |

## Limitations {#limitations}

- Narrative review
- No quantitative synthesis in abstract

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7222019517 for doi:10.1016/j.cosrev.2026.101081 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/mamsheli-2026-eeg-ai-faces.md
================================================================================

---
type: source
id: mamsheli-2026-eeg-ai-faces
doi: 10.1145/3822301.3822351
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#mamsheli-2026-eeg-ai-faces
---

# Processing of Real and AI-Generated Human Faces: An Exploratory EEG Study

> **Source record** `mamsheli-2026-eeg-ai-faces` · 2026 · Proceedings of the European Conference on Cognitive Ergonomics (ECCE 2026)  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Real and AI-generated faces evoked different early brain responses (N170, N400, EPN) but not a different late LPP, and were rated differently for expressiveness and 'sense of self', while perceived intelligence and appeal did not differ.

## Citation {#citation}

Saryz Taha Mamsheli, Amanda Tunes Paulsen, Enrico Cipriani, Simone Grassini (2026). Processing of Real and AI-Generated Human Faces: An Exploratory EEG Study. *Proceedings of the European Conference on Cognitive Ergonomics (ECCE 2026)*, 375-379. https://doi.org/10.1145/3822301.3822351

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#mamsheli-2026-eeg-ai-faces](/data/citations.json#mamsheli-2026-eeg-ai-faces)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Exploratory EEG (ERP) study with post-recording questionnaire; poster analyses restricted to real vs AI-generated human faces |
| Modality | image-face |
| Stimuli | Images of humans, AI-generated humans, robots and dolls with happy, fearful and neutral expressions (generator not stated in abstract) |
| Participants (N) | 28 |
| Topics | uncanny-valley, neural, implicit-detection |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| ERP differences, real vs AI-generated faces | significant for N170, N400 and EPN; not for LPP | early stages differentiate the faces |
| Questionnaire: expressiveness; sense of self and inner consciousness | significantly different between real and AI faces |  |
| Questionnaire: perceived intelligence and appeal | n.s. |  |

## Mechanism relevance {#mechanism}

`implicit-sensitivity`

## Limitations {#limitations}

- Exploratory poster analyses
- Small N
- Generator not stated in abstract
- Values from abstract only

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7212435617 for doi:10.1145/3822301.3822351 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/hsieh-2026-robot-gaze-uncanny.md
================================================================================

---
type: source
id: hsieh-2026-robot-gaze-uncanny
doi: 10.1016/j.chbr.2026.101378
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#hsieh-2026-robot-gaze-uncanny
---

# Robot gaze-timing combinations and the uncanny valley

> **Source record** `hsieh-2026-robot-gaze-uncanny` · 2026 · Computers in Human Behavior Reports  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** Across nine robot gaze-timing conditions, likability followed an uncanny-valley (cubic) curve over perceived anthropomorphism while perceived empathy rose steadily; extraversion, negative attitudes and neuroticism moderated these relations.

## Citation {#citation}

Te-Yi Hsieh, Pai-He Hsiao, Sin-Cheng Ciou, Chia-Hui Pan, Tzu-Chun Chen, Chia-Chun Tsai, Yu Fang, Su-Ling Yeh (2026). Robot gaze-timing combinations and the uncanny valley. *Computers in Human Behavior Reports*, 24, 101378. https://doi.org/10.1016/j.chbr.2026.101378

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#hsieh-2026-robot-gaze-uncanny](/data/citations.json#hsieh-2026-robot-gaze-uncanny)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Within-subject ratings of a virtual robot in nine gaze conditions (direct vs averted gaze ratios and durations) during storytelling, with eye tracking (EMHMM) and personality/attitude measures |
| Modality | robot-face |
| Stimuli | Virtual anthropomorphic robot, nine gaze-timing combinations |
| Participants (N) | 61 |
| Topics | uncanny-valley, valley-shape, individual-differences |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Anthropomorphism-likability relation | cubic, uncanny-valley pattern |  |
| Anthropomorphism-empathy relation | rose steadily (no valley) |  |
| Moderators (exploratory) | extraversion and negative attitudes toward robots (likability); neuroticism (empathy) |  |
| Eye-movement patterns (EMHMM) | two: analytic and holistic | higher openness → more analytic viewing of the highest-rated gaze conditions |

## Moderators {#moderators}

- Personality (extraversion, neuroticism, openness)
- Attitudes toward robots

## Mechanism relevance {#mechanism}

`human-likeness`

## Limitations {#limitations}

- Virtual robot, not synthetic human media
- Values from abstract only
- Exploratory moderator analyses

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7221007893 for doi:10.1016/j.chbr.2026.101378 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.


================================================================================
URL: https://research.breacher.ai/sources/jang-2026-uncanny-conversation.md
================================================================================

---
type: source
id: jang-2026-uncanny-conversation
doi: 10.3390/bs16101794
evidence_tier: B
verification: verified-abstract
citations_entry: https://research.breacher.ai/data/citations.json#jang-2026-uncanny-conversation
---

# Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust

> **Source record** `jang-2026-uncanny-conversation` · 2026 · Behavioral Sciences  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** A machine-like AI counsellor was liked and trusted most at low self-disclosure and less at high disclosure (an uncanny-valley-like curve of mind), whereas a human-like counsellor gained with deeper disclosure; internal and external human-likeness interact.

## Citation {#citation}

Jungwoo Jang, Jae-gil Lee, Hayeon Song (2026). Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust. *Behavioral Sciences*, 16(10), 1794. https://doi.org/10.3390/bs16101794

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#jang-2026-uncanny-conversation](/data/citations.json#jang-2026-uncanny-conversation)

## Study design {#design}

| Field | Value |
|---|---|
| Design | 2 (appearance: machine-like vs human-like) × 3 (self-disclosure depth: none, low, high) between-subjects experiment; one scripted counselling session with an LLM-style AI counsellor prototype; six outcome measures, controlling for prior mind perception |
| Modality | text |
| Stimuli | AI counsellor prototype varying appearance and depth of its own self-disclosure |
| Participants (N) | 193 |
| N detail | University students |
| Topics | uncanny-valley, mind-perception, trust |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Machine-like counsellor: intimacy, social presence, trust, authenticity | peaked at low disclosure, declined at high | uncanny-valley-like pattern |
| Human-like counsellor | evaluations generally rose with disclosure depth |  |
| Perceived inappropriateness | not higher for deeper disclosure |  |
| Mediation (machine-like only) | deeper disclosure → lower anthropomorphism → lower intimacy and trust |  |

## Moderators {#moderators}

- Agent appearance

## Mechanism relevance {#mechanism}

`mind-perception`

## Limitations {#limitations}

- Student sample
- Single scripted session
- Values from abstract only

## Cited in {#cited-in}

- [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7215112744 for doi:10.3390/bs16101794 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.