The researchers trained participants in the studies by drawing their attention to six perceptual qualities:
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Symmetry – AI often fails to recreate the quirks that make us human – a slightly drooping eyelid or a lop-sided smile. “If it’s too good to be true, it probably isn’t.”
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Proportionality – A similar concept. Very large noses or protruding ears are not typical of deepfake images.
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Attractiveness – “AI faces tend to look more attractive,” explains Sutherland. “That one is more subjective, an aesthetic judgement, but AI often creates faces that are pleasant looking.”
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Distinctiveness – “That could be something like ‘what would make a face stand out in a crowd?’ AI faces do tend to cluster towards the average. So they look a bit more generic.”
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Expressiveness – “AI faces tend to look less emotionally expressive”, says Sutherland. “They tend to show less emotion.”
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Memorability – “They often look less memorable – they’re difficult to remember.”
AI also tends to be less proficient at recreating non-white, older or younger faces because more of its training involves young white people.
Some of these tips might sound quite similar and “fuzzy” – but that’s the point.
Rarely will you encounter a surefire “tell” that will unmask an AI fake. Rather, it is about becoming attuned to their characteristics and developing a gut feeling.
Researchers found that by exposing people to images, both AI and real, then telling them which was which, they can get significantly better at it – even in the space of an hour or so.
The researchers found the participants would typically increase their accuracy score from about 40% to 80%.
A few individuals achieved close to 100% accuracy.