New study examines why some people can more easily detect AI imagery
Can you tell the difference between an artificial-intelligence-generated face and a real one? In an era of digital misinformation, where fabricated images can spread widely across news and social media, this skill is proving invaluable.
A new study has found that a person’s object recognition ability, or the ability to distinguish visually similar objects, can predict who can spot an AI-generated face. The higher the ability, the easier it is for a person to tell the difference. The study was authored by Isabel Gauthier, David K. Wilson Chair and Professor of Psychology, Jason Chow, Ph.D.’24, and Rankin McGugin, former research assistant professor in the Department of Psychology.
This discovery highlights the importance of general object recognition and suggests that such abilities may play a crucial role in helping society better prepare for emerging forms of digital deception. By uncovering the traits that make some people less vulnerable to AI-generated misinformation, this work advances understanding human perception in the age of AI.
“These results highlight a visual ability that has very general applications,” Gauthier said. “It’s a stable trait that helps people meet new perceptual challenges, including those created by AI. We were shocked to see how intelligence or even technology training did not help accurately judge if a face is AI.”
In the study, researchers developed the AI Face Test, the first tool designed to measure individual differences in this skill. They found that traditional factors such as intelligence, experience with AI, or even specialized face recognition skills did not predict who could reliably tell real from fake. Instead, the strongest predictor was objection recognition.
“We were interested not just in examining whether people are able to differentiate between a real face and an AI-generated face, but in comparing people on their ability to perform this task and see if we could predict the performance using object recognition,” Gauthier said. “This approach is very novel—there’s not a lot of people who study individual differences in object recognition. In vision, there’s a tradition of looking at the average of a group. Nobody has been asking these questions, and we have a lot to learn about how people do these things.”
Humans with stronger object recognition skills consistently outperformed others in identifying AI-generated faces, and their performance remained stable when re-tested. This same ability has been linked in other research to performance in diverse tasks, such as identifying lung nodules in chest X-rays, categorizing blood cells as cancerous, recognizing musical notation, and even judging sex from retinal images.
The findings show that a broad visual ability, not tied to faces or technology experience, helps some individuals navigate the unprecedented challenge of distinguishing real from synthetic images.
“There is this general message we hear in the media that AI images are so realistic that we can’t tell the difference, and I think that’s misleading,” Gauthier said. “I think there’s a lot of messaging indicating that we can’t differentiate, when in fact, what you have is a distribution of people. There are some who can’t tell the difference, and then there are some who are doing it great, and then there’s some who are doing it okay. As AI becomes ever present in our reality, I think it’s useful to know that some people are better at this than others.”