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
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
Study 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
| 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
- Face-recognition ability
Limitations
- White faces and StyleGAN2 only
- Effect size reported as d = 0.55 in abstract and 0.50 in results
Cited in
- MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- MR-0002: The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?
Verification
| Field | Value |
|---|---|
| Status | verified-fulltext |
| Compiler confidence | high |
| Checked on | 2026-10-12 |
| Via | UNSWorks repository submitted version (hdl 1959.4/107078) |