{
 "version": "https://jsonfeed.org/version/1.1",
 "title": "Deepfake Research Index",
 "home_page_url": "https://research.breacher.ai/",
 "feed_url": "https://research.breacher.ai/feed.json",
 "description": "Agent-readable digests of peer-reviewed research on deepfakes, synthetic media, and human perception.",
 "language": "en",
 "authors": [
  {
   "name": "Breacher.ai"
  }
 ],
 "items": [
  {
   "id": "https://research.breacher.ai/changelog.md#2026-10-19",
   "url": "https://research.breacher.ai/changelog.html#2026-10-19",
   "title": "2026-10-19 · v0.4.0 · Citations file, MR-0003 voice clones, expert readers and new uncanny-valley studies",
   "content_text": "- **Site:**\n  - 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.\n  - 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.\n  - 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.\n  - 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.\n  - 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.\n  - 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.\n- **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.\n- **Added 6 source records:**\n  - [lee-2026-ai-chest-radiographs](/sources/lee-2026-ai-chest-radiographs.md): tier B, verified-abstract.\n  - [chen-2026-humans-ai-deepfake-teaming](/sources/chen-2026-humans-ai-deepfake-teaming.md): tier B (HFES proceedings), verified-abstract; abstract reports no figures.\n  - [naseem-2026-in-the-wild-detection-review](/sources/naseem-2026-in-the-wild-detection-review.md): tier C (narrative review), verified-abstract.\n  - [mamsheli-2026-eeg-ai-faces](/sources/mamsheli-2026-eeg-ai-faces.md): tier B (ECCE proceedings, exploratory), verified-abstract.\n  - [hsieh-2026-robot-gaze-uncanny](/sources/hsieh-2026-robot-gaze-uncanny.md): tier B, verified-abstract.\n  - [jang-2026-uncanny-conversation](/sources/jang-2026-uncanny-conversation.md): tier B, verified-abstract.\n- **[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.\n- **[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).\n- **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.\n- **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.\n- **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).",
   "date_published": "2026-10-19T12:00:00Z"
  },
  {
   "id": "https://research.breacher.ai/metareports/human-detection-accuracy.md#v0.4.0",
   "url": "https://research.breacher.ai/metareports/human-detection-accuracy.html",
   "title": "MR-0001 v0.4.0: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training",
   "content_text": "How accurately can people tell AI-generated faces, voices, and videos from real ones, and what changes that accuracy?",
   "date_published": "2026-10-19T12:00:00Z",
   "tags": [
    "deepfakes",
    "human-detection",
    "hyperrealism",
    "overconfidence",
    "training",
    "voice-cloning",
    "security-awareness"
   ]
  },
  {
   "id": "https://research.breacher.ai/metareports/uncanny-valley-and-synthetic-faces.md#v0.4.0",
   "url": "https://research.breacher.ai/metareports/uncanny-valley-and-synthetic-faces.html",
   "title": "MR-0002 v0.4.0: The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?",
   "content_text": "Is the uncanny valley a real, measurable effect; which mechanisms explain it; and does it still help people notice AI-generated faces and voices?",
   "date_published": "2026-10-19T12:00:00Z",
   "tags": [
    "uncanny-valley",
    "perception",
    "neuroscience",
    "hyperrealism",
    "mind-perception",
    "synthetic-faces",
    "deepfakes"
   ]
  },
  {
   "id": "https://research.breacher.ai/metareports/voice-cloning-human-detection.md#v0.1.0",
   "url": "https://research.breacher.ai/metareports/voice-cloning-human-detection.html",
   "title": "MR-0003 v0.1.0: Voice Clones and Synthetic Speech: How Well Do People Detect Them?",
   "content_text": "Can people tell cloned or synthetic voices from real speakers, and what changes that ability?",
   "date_published": "2026-10-19T12:00:00Z",
   "tags": [
    "voice-cloning",
    "synthetic-speech",
    "vishing",
    "human-detection",
    "audio-deepfakes"
   ]
  },
  {
   "id": "https://research.breacher.ai/changelog.md#2026-10-18",
   "url": "https://research.breacher.ai/changelog.html#2026-10-18",
   "title": "2026-10-18 · v0.3.0 · Aggregation limits, plausibility, cross-linguistic speech and AI-text training",
   "content_text": "- **Added 6 source records** (all tier B, peer-reviewed):\n  - [kramer-2026-aggregation-human-ai](/sources/kramer-2026-aggregation-human-ai.md): verified-abstract.\n  - [plohl-2026-political-deepfakes-plausibility](/sources/plohl-2026-political-deepfakes-plausibility.md): verified-abstract.\n  - [san-segundo-2026-audio-language-style](/sources/san-segundo-2026-audio-language-style.md): verified-abstract.\n  - [manhede-2026-online-markets-training](/sources/manhede-2026-online-markets-training.md): verified-fulltext.\n  - [sulimani-2026-recognition-ai-faces](/sources/sulimani-2026-recognition-ai-faces.md): verified-abstract.\n  - [yeung-2026-isthisai-strategies](/sources/yeung-2026-isthisai-strategies.md): verified-abstract (archival conference, AIES).\n- **[MR-0001](/metareports/human-detection-accuracy.md) v0.3.0:**\n  - 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).\n  - 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).\n  - 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.\n- **[MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md) v0.3.0:** F9 revised (AI faces remembered better than real faces, covertly).\n- **Homepage:** interventions table (AI advice, labelled training) and metareport table updated.\n- **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.\n- **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).",
   "date_published": "2026-10-18T12:00:00Z"
  },
  {
   "id": "https://research.breacher.ai/changelog.md#2026-10-12",
   "url": "https://research.breacher.ai/changelog.html#2026-10-12",
   "title": "2026-10-12 · v0.2.0 · Voice clones, training and individual differences",
   "content_text": "- **Added 7 source records** (all tier B, peer-reviewed):\n  - [ivory-2026-faces-voices-domain](/sources/ivory-2026-faces-voices-domain.md): verified-fulltext.\n  - [lavan-2025-voice-clones-hyperrealism](/sources/lavan-2025-voice-clones-hyperrealism.md): verified-fulltext.\n  - [pehlivanoglu-2026-machines-humans](/sources/pehlivanoglu-2026-machines-humans.md): verified-fulltext (Study 1; Study 2 from abstract).\n  - [dunn-2026-super-recognizers-average](/sources/dunn-2026-super-recognizers-average.md): verified-fulltext (repository version).\n  - [rehman-2025-training-synthetic-faces](/sources/rehman-2025-training-synthetic-faces.md): verified-fulltext.\n  - [dawel-2026-training-ai-faces](/sources/dawel-2026-training-ai-faces.md): verified-abstract.\n  - [kramer-2024-crowds-synthetic-faces](/sources/kramer-2024-crowds-synthetic-faces.md): verified-abstract.\n- **Verification upgrades:**\n  - [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%).\n  - [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.\n- **[MR-0001](/metareports/human-detection-accuracy.md) v0.2.0:**\n  - 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).\n  - 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).\n- **[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).\n- **Homepage:** key fact 9, accuracy table, myths, interventions and open questions updated for voice clones and training.\n- **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.\n- **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).",
   "date_published": "2026-10-12T12:00:00Z"
  },
  {
   "id": "https://research.breacher.ai/changelog.md#2026-10-07",
   "url": "https://research.breacher.ai/changelog.html#2026-10-07",
   "title": "2026-10-07 · v0.1.0 · Initial seed release",
   "content_text": "- **Added metareport** [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes. 11 findings (F1–F11) from 17 sources.\n- **Added metareport** [MR-0002](/metareports/uncanny-valley-and-synthetic-faces.md): The Uncanny Valley and Synthetic Media. 12 findings (F1–F12) from 22 sources.\n- **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.\n- **Added** [publisher disclosure page](/about/publisher.md), describing Breacher.ai's services. It is kept separate from the research pages.\n- **Verification at release:**\n  - 9 records checked against full text.\n  - 12 checked against the official abstract.\n  - 2 with metadata/DOI only.\n  - 12 pending.\n- **Corrections made during verification, before release:**\n  - Mai et al. 2023: 73% is the hit rate on fakes. Overall single-clip accuracy is 70.35%.\n  - Diel et al. 2022: overall effect g = 1.01 and face distortion g = 1.46. This replaced a mischaracterization of the overall trend.\n  - Cheetham et al. 2014: ambiguous faces were *easier* to discriminate.\n  - Eiserbeck et al. 2023: the LPP effect appears for presumed-fake smiles. Early components were lost.\n  - Schindler et al. 2017: EPN showed no realism effect.\n  - Rosenthal-von der Pütten et al. 2019: DMPFC encodes choices about artificial agents.\n  - Groh et al. 2022: N = 15,016.\n  - Cooke et al.: now published in *Communications of the ACM* (2025).",
   "date_published": "2026-10-07T12:00:00Z"
  }
 ]
}