Artificial intelligence may be better than people at spotting fake faces in photos — but humans still have the upper hand when those fakes start moving.
In a large recent study, psychologists and computer scientists at the University of Florida found that AI programs were up to 97% accurate at detecting pictures of deepfake faces. Participants in the study performed no better than chance.
However, the algorithms’ performance declined sharply when it came to detecting deepfake videos. In those tests, programs performed at chance levels, while humans correctly identified real and fake videos about two-thirds of the time. Human participants appeared to pick up on subtle inconsistencies in movement, facial expressions and timing — cues the algorithms struggled to interpret.
As sophisticated fake images and videos, known as deepfakes, continue to improve and spread widely online, distinguishing real from AI imagery becomes more important.
“The significant decisions that are made by individuals and governments need to be based on real and accurate information. We need to know if people can tell what’s real or not as the technology gets more sophisticated at fooling us,” said Brian Cahill, Ph.D., a professor of psychology at UF and co-author of the study.
Cahill collaborated on the study with researchers across UF, including the Florida Institute for National Security’s Didem Pehlivanoglu, Ph.D., and Mengdi Zhu, Ph.D., along with senior author and Professor of Psychology Natalie Ebner , Ph.D. Their study was published Jan. 7 in the journal Cognitive Research: Principles and Implications.
The researchers created and curated hundreds of real and fake images and videos featuring static faces and people talking. Thousands of participants were then asked to rate the reality of the images. The same images and videos were then put through algorithms designed to separate real images from fake ones.
The findings suggest that for still images, automated detection tools may now outperform human judgment alone. But people still have an advantage when it comes to identifying deepfake videos.
“I think we were all a little shocked to see humans outperform AI on videos,” Cahill said. “But the videos have more cues, it’s a richer context. There’s more stuff for the human brain to pick up on.”
People’s abilities and even mood made a difference in how well they detected deepfake videos. Perhaps unsurprisingly, those who scored higher in analytical thinking and internet skills were better at detecting AI-generated videos. Participants who reported being in a better mood performed worse at detecting deepfake videos, which may reflect greater trust when feeling positive.
The study tested specific types of faces and videos under controlled conditions, which may not reflect the full complexity of real-world online content. And both AI systems and deepfake technology are evolving rapidly, meaning the balance between humans and machines could shift again.
The unfortunate reality, the authors note, is that with deepfake imagery rapidly progressing, determining truth online requires increasing vigilance from everyone.
“We don’t necessarily need to be able to detect everything ourselves,” Zhu said. “But we do need to stay alert, question what we see and look for evidence to support it.”
Cognitive Research Principles and Implications
Experimental study
People
Is this real? Susceptibility to deepfakes in machines and humans
7-Jan-2026