Five minutes of training could help you spot fake AI faces
12 November 2025
Five minutes of training can significantly improve people's ability to identify fake faces created by artificial intelligence, new research shows.
Scientists from the University of Reading, Greenwich, Leeds and Lincoln tested 664 participants' ability to distinguish between real human faces and faces generated by computer software called StyleGAN3. Without any training, super-recognisers (individuals who score significantly higher than average on face recognition tests) correctly identified fake faces 41% of the time, while participants with typical abilities scored just 31%. If they had their eyes closed and guessed, people would perform at around 50% (chance level).
A new set of participants who received a brief training procedure, which highlighted common computer rendering mistakes such as unusual hair patterns or incorrect numbers of teeth, had higher accuracy. Super-recognisers achieved 64% accuracy in detecting fake faces, while typical participants scored 51% accuracy.
Dr Katie Gray, lead researcher at the University of Reading, said: "Computer-generated faces pose genuine security risks. They have been used to create fake social media profiles, bypass identity verification systems and create false documents. The faces produced by the latest generation of artificial intelligence software are extremely realistic. People often judge AI-generated faces as more realistic than actual human faces.
“Our training procedure is brief and easy to implement. The results suggest that combining this training with the natural abilities of super-recognisers could help tackle real-world problems, such as verifying identities online."
Advancing software poses a tough challenge
The training affected both groups equally, suggesting super-recognisers may use different visual cues than typical observers when identifying synthetic faces, rather than simply being better at spotting rendering errors.
The research, published today (Wednesday, 12 November in Royal Society Open Science) tested faces created by StyleGAN3, the most advanced system available when the study was conducted. This represents a significant challenge compared to earlier research using older software, as participants in this study tended to have poorer performance than those in previous studies. Future research will examine whether the training effects last over time and how super-recognisers' skills might complement artificial intelligence detection tools.
Notes to editors:
Dr Katie Gray is available for interview. Contact the University of Reading Press Office on 0118 378 5757 or pressoffice@reading.ac.uk
Download hi-res images of the faces studied in the research via Google Drive.
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