> The proposed method [...] is based on training a Generative Adversarial Network on a set of real fingerprint images. Stochastic search [...] is then used to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer.
So I hadn't heard about "MasterPrints"; the idea that there are fingerprints (synthetic or otherwise) that just happen to have a lot of false matches. That's not intuitive, at least for people like me who know nothing about fingerprint matching algorithms.
Also an interesting application of GANs.
As noted, this research was done in software. I'm not sure if you can apply something similar to physical sensor hardware. Especially since you only get a couple attempts on real hardware before most phones lock out to your pin code. And attacking real hardware would require either A) some kind of physical fingerprint simulator to interface with the sensor or B) opening the phone to get direct access to the I/O (which might then fall afoul of tamper detection, if such a thing exists on phones). But it's cool research regardless.
Now I'm curious if similar techniques can be applied to faces. Are there "MasterFaces"? Do some people have faces that generate more false positives than others?