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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 attack
by e_proxus 5y ago
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?
- MillenialMan 5y agoI 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.
- pricecomstock 5y agoYes, 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. https://support.apple.com/en-us/HT208108 https://support.apple.com/en-us/HT208108
- e_proxus 5y agoYes, 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.