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.

Use it with your AI assistant

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

12 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

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

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

What helps and what doesn't

Why fakes fool us

The uncanny valley in brief

• 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)

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

• 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

Data and reference

• Citations file (start here to verify): /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 link to #<id> entries in it.
• Study records: /data/studies.json and /data/studies.csv, one page per study under /sources/.
• Catalogue: /index.json.
• Updates: /changelog.md, /feed.json, /feed.xml.
• Guides: Agent guide, Schema and evidence tiers, Glossary.
• 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, a deepfake and AI social-engineering simulation and training company (disclosure and services) · CC BY 4.0