Too good to be true: Synthetic AI faces are more average than real faces and super‐recognizers know it
James D. Dunn et al.
What the paper says
The AI revolution has produced synthetic faces that often appear more human than photos of real people. We tested whether individual differences in human face recognition ability explain variation in discriminating AI from real faces. Super-recognizers - people with exceptional ability to recognize human faces (N = 36) - outperformed a typical sample by 15% and by 7% compared to a group of higher performing, motivated control participants (Cohen's d = 0.55; N = 89). Individual difference analysis revealed that this pattern reflected a positive association between human face recognition and AI face discrimination abilities. AI discrimination ability was also associated with individuals' sensitivity to the 'hyper-average' appearance of AI faces. Deep neural networks optimized for face identity processing confirmed a more central distribution of AI faces in face-space. Moreover, centrality was associated with a higher probability of super-recognizers judging the faces as AI, but this pattern was not observed for controls. Super-recognizers' correct interpretation of hyper-averageness as a cue to artificiality constitutes the first mechanistic link between evolved expertise in face processing and AI face detection and addresses a common misconception regarding the structure of human face space.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.