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
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
Study 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
| 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
- Detection-improvement strategies raised performance above chance, with strongest effects for video.
Limitations
- Heterogeneous paradigms and generators
- Many included studies use pre-2022 generators
- Wide CIs, especially audio
Cited in
- MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- MR-0003: Voice Clones and Synthetic Speech: How Well Do People Detect Them?
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.