The StyleGAN2 paper[0] actually addresses some of the biggest clues. They didn't completely solve everything but symmetry is one of the biggest indicators. Specifically ears and eyes. But also really pay attention to the teeth. If you go to ThisPersonDoesNotExist[1] you'll see that there's still phase issues with teeth and eyes. It's an improvement on the original paper but it is still weird. For eyes you'll notice things like them not pointing in the same direction (this isn't always super obvious). Another big thing to look at is the neck. Sometimes women will get Adam's Apples and men will lack them. Collars won't be symmetric and necks can have weird wrinkles. Lastly pay close attention to the background because that's where you'll likely find monsters. Lots of portrait photos have blurred background so "lack of detail" isn't a great metric. But backgrounds that are a bit disjoint are great indicators. None of these metrics alone is a great tool and you gotta use them together (unless someone messed up big time when selecting the photo). Poke around through [1] a bit and you'll start seeing some of the features I'm discussing. But I should also note that these flaws are known and being worked on so this information's usefulness will degrade with time (it's still good information because it helps us know how to look and what's difficult about faces).

These of course go along with standard identification techniques like lighting, shadows, etc.

[0] https://arxiv.org/abs/1912.04958

[1] https://thispersondoesnotexist.com/

Anywhere with high gradient variability is where to look for irregularities. Ears... because of earrings. Around the eyes... because of glasses. Hair accessories, necklaces, locks of hair, borders with clothing, they're all giving away most GAN faces.

So you just need a GAN trained with a dataset that excludes earrings and glasses. Then maybe another GAN trained to add earrings and glasses onto faces that don't have any :)

radar guns and radar detectors