UVic researchers looking to help people spot deepfake AI images
Published 5:00 am Tuesday, June 30, 2026
A recent University of Victoria study suggests that humans have been successfully trained to spot computer-generated faces from from photos of real human faces – also known as deepfakes.
The UVic Different Minds Lab team, led by psychology professor Jim Tanaka and post-doctoral fellow Eric Mah, partnered with the Australian National University (ANU) to develop a quick, effective and robust training technique for improving people’s detection of deepfake faces, in research paper published by Proceedings of the National Academy of Sciences.
AI-generated deepfake faces have become so realistic that it is difficult for people to tell them apart from photos of real humans, contributing to increases in AI-related fraud, according to a UVic release. The researchers trained people to spot AI-generated faces by drawing their attention to six perceptual qualities: distinctiveness, memorability, proportionality, symmetry, attractiveness and expressiveness.
“It was amazing to see the dramatic improvement in people’s ability to detect AI faces,’’ noted Amy Dawel, associate professor and director of the ANU lab in a news release. “We’ve shown our training is effective for some of the most convincing fakes available – StyleGAN faces. Now we need to find out whether that training generalises to other AI-generated faces.”
“What surprised us was how quickly people improved in less than an hour of training, AI face detection accuracy increased by nearly 30 per cent, and participants not only became more accurate, they became faster too,” added Tanaka in the release. “Our results show that AI detection can be trained up like other forms of perceptual expertise.”
The researchers said AI image-generation technology is improving extremely quickly, and many people underestimate how convincing these faces can be, so the hope is this work can help people navigate increasingly complex online environments.
