Re:GAN-generated facial images that are capable of impersona...
by e_proxus on Wednesday August 04, 2021 @10:31AM
(#28061425)
Unless you can either produce the correct 3D surface itself or fool the sensors somehow I don’t see how the inner implementation matters.
Or do you mean attacking the inner network somehow from inside the system?
Re:GAN-generated facial images that are capable of impersona...
by MillenialMan on Wednesday August 04, 2021 @10:55AM
(#28061722)
I mean you train your network to produce images that translate into adversarial 3d surfaces.
You don't need to produce the correct 3d surface if the surface recogniser is neural - you just need to produce a 3d surface that's adversarial. The adversarial surface could be completely unrealistic, like these adversarial images. (Although the adversarial generator could also be trained with "realism" as a constraint.)
Are they able to detect depth independent of the surface of a presented image? That would make it harder, but the point of failure then is just figuring out a way to dynamically fool them. I wouldn't be confident saying that's impossible.
Re:GAN-generated facial images that are capable of impersona...
by pricecomstock on Wednesday August 04, 2021 @12:24PM
(#28062916)
Yes, FaceID uses actual depth/distance data by projecting IR dots during scanning. So you would either need to very precisely mock these somehow, or create an actual 3D surface.
Re:GAN-generated facial images that are capable of impersona...
by e_proxus on Wednesday August 04, 2021 @12:27PM
(#28062954)
Yes, Face ID uses infrared depth sensors so it shouldn’t be possible to use just a printed image. You might be able to fool it with by printing with some strange material that fools them, but I don’t see the point with coming up with such an advanced technique. Then you might as well just print a 3D model.
All neural nets are vulnerable to adversarial examples. It's a fundamental property they hold, because they're essentially stacked linear models. So (for example) they get more confident about their predictions when given a sufficiently out-of-domain input - adversarial training is essentially just finding paths that trigger an out-of-domain response.
I don't see how an additional transformation before input precludes that.