spot AI-generated – A new study finds that the old trick—spotting obvious visual glitches in AI portraits—isn’t keeping up. Instead, researchers say short training sessions can teach people to recognize broader, “typical” patterns that AI faces share, boosting detection accuracy
For years, AI-generated faces had a tell. There was usually that uncanny sheen, skin that looked too smooth, eyes that didn’t quite match what a human would do—sometimes even a conspicuous third ear. It was the kind of wrongness you could shrug off as fake.
That’s changed. With modern image generators, portraits can be so convincing that even careful observers struggle to separate fact from figment. The stakes have shifted from embarrassing mistakes to identity itself. Apps such as Zoom and Tinder allow users to submit biometric identification. such as retinal scans. to help prove a real person exists behind a profile picture.
A new study suggests there’s still something human beings can do—train their attention.
Past attempts to teach people to spot AI faces tended to focus on visual glitches or statistical fingerprints left behind by a particular generator. The idea was simple: look for the wonky ear, the eye with two pupils, the inconsistent details that would stand out.
But those cues can vanish. A software update can smooth them away. Switching prompts can steer the image generation somewhere else entirely. “The AI is getting too good. ” said Amy Dawel. an associate professor at Australian National University and the lead author on the study. in a press release. “And fraudsters may avoid using pictures with obvious flaws anyway.”.
The researchers didn’t try to chase disappearing artifacts. Instead, they taught participants to recognize broader patterns in how AI systems generate images. “Our training directs people’s attention to global qualities that differ between AI and human faces,” Dawel said.
Current AI image generators are trained on datasets composed of millions of images. When prompted to generate a face, they don’t copy any specific person. They compose a new face based in part on mathematical patterns shared across the data set—patterns that help the system construct a “typical” human face.
That “typical” pull shows up in ways people can learn to notice. AI-generated faces often drift toward statistical averages. They aren’t always overly unrealistic. They’re a little too balanced, a little too generic, a little too conventional. None of these traits alone may raise a red flag. Together. the images can feel blander than the sum of their parts—banal in a way humans can often sense even if they can’t always name why.
The study tested a different approach to detection. Rather than hunting for fleeting artifacts like malformed ears or mismatched jewelry, the training pointed participants toward six markers. Compared with real faces. AI-generated faces tend to be more symmetrical. more proportional. and more attractive—while also being less expressive. less distinctive and significantly less memorable. When the researchers trained participants to look for those six markers, their ability to spot the AI face almost doubled.
Even relatively short training sessions helped. Tanya George. a student researcher at Australian National University who trained the study’s participants. said. “Even relatively short training sessions helped participants improve their accuracy.” She added: “Research like this can help people navigate increasingly complex online environments.”.
There’s a broader lesson in what the faces look like and what they lack. AI tends to gravitate toward the center, toward averages. Real people do not. Our faces are shaped by countless small deviations from the norm—subtle asymmetries. distinctive features. and expressions that make a person memorable.
Those imperfections aren’t defects. They’re a signature.
In a world where AI portraits can slide past the old tells, the study’s most practical message is also its most human: you don’t have to “spot a glitch” to catch a fake. You can learn what patterns tend to come with the machine’s version of a face—and practice seeing them before they disappear.
AI-generated faces biometric identification retinal scans online fraud image generators facial recognition Australian National University Amy Dawel Tanya George media literacy