It's a constant cat-and-mouse game. When I worked in this space (2019-2021), the best defense against deep fakes was looking at the microfacial behavior/kinemat

It's a constant cat-and-mouse game. When I worked in this space (2019-2021), the best defense against deep fakes was looking at the microfacial behavior/kinematics of the "puppetmaster" and comparing against known standards of the deepfake subject. Works even if the fake is pixel-perfect (since it looks at the facial "wireframe" rather than the image itself). The obvious downside is you need sample data of the subject (and usually tons of it). I wonder if that general approach can be optimized. E.g. Deep fakes tend to struggle with certain fine movement/detail, if you had a reflection of the subject, the algorithm would have to not just replicate the main face and the mirror, but also be completely optically consistent. Was a fun project, but the cat-and-mouse feeling was inescapable. For those curious, look up the DARPA MediFor project. Siwei Lyu (in the article) did a bunch of work in this space. Also see Hany Farid and Shruti Agarwal. They've worked specifically with deep fake detection. https://arxiv.org/abs/2004.14491 https://arxiv.org/abs/2004.14491