It used to be easy to tell when a face was generated with artificial intelligence (AI). Whether it was a distinctive uncanny sheen, impossibly smooth skin, eyes that didn’t quite make sense or a conspicuous third ear, older AI models’ facsimiles of human faces were simple to spot and easy to dismiss. That’s just not true anymore.
Now, AI image generators can produce portraits so convincing that even careful observers struggle to distinguish fact from figment. That’s why apps such as Zoom and Tinder allow their users to submit biometric identification, such as retinal scans, to help prove that a real person exists behind a profile picture. But a new study suggests you can train your brain to get better at spotting fakes.
Past attempts to teach people to spot AI faces have focused on training viewers to look for visual glitches or statistical fingerprints left behind by a particular image generator, such as a wonky ear or an eye with two pupils. The problem is that those clues can disappear with a software update or by simply using a different prompt. “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 result is an endless technological arms race humanity seems destined to lose.
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Instead, the researchers taught the participants how 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 themselves trained on datasets composed of millions of images. When prompted to generate a face, they don’t copy specific faces, but instead compose a new face that is based in part on the mathematical patterns shared across the faces in that data set—these allow the AI to construct a “typical” human face.
The result is that AI-generated faces often drift toward statistical averages. They’re not overly unrealistic, so much as a little too balanced, a little too generic, and a little too conventional. Individually, none of these traits are necessarily suspicious. But together, the whole is blander than the sum of its parts—a subtle banality humans can often implicitly sense.
“Even relatively short training sessions helped participants improve their accuracy,” says Tanya George, a student researcher at Australian National University who trained the study’s participants. “Research like this can help people navigate increasingly complex online environments.”
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 these six markers instead of fleeting artifacts like malformed ears or mismatched jewelry, their ability to spot the AI face almost doubled.
In other words, AI gravitates to the center. Real people do not. Our faces are shaped by countless small deviations from the norm—our subtle asymmetries, distinctive features, and expressions make us memorable. Those imperfections are not flaws. They are our signature.
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