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Really interesting work. Snippet from the paper's summary for the lazy: > The proposed method [...] is based on training a Generative Adversarial Network on a
by fpgaminer 8y ago
Really interesting work. Snippet from the paper's summary for the lazy:
> 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?
- pbhjpbhj 8y agoPresumably an eigen-face acts as a master-face of sorts?
- NarcolepticFrog 8y agoWhy would it? Eigen-faces tend to not even look like faces (which makes sense, given that the goal is to represent a collection of real faces as linear combinations of the orthogonal eigen-faces). One possibility would be to generate random faces as random linear combinations of the eigen-face basis, but this type of attack could be thwarted by rate limiting. A real "master face" would be a face that looks like many people, and it seems like you could try to obtain faces like that by playing a two-player game between a recognizer and a face generator (as is done when training GANs).
- c3534l 8y agoThe images that screw up image recognition systems to get very high confidence of very wrong classifications don't look like the thing they're mistaken for at all. All the GANs I've seen produce what looks like colorful static to a human eye. I'm not sure where you're getting the info that it would look like an average face because I've never seen ML systems defeated with GANs like that.
- ggggtez 8y agoYes, but you need more than one face. An average face doesn't much look like any particular person. You'd need maybe a few hundred faces. Facial matching usually reduces search space by assuming something about the input. I.e. you don't match a face against the whole world, just the friends of a person uploading a photo. Otherwise you'd run into matching a similar face to some stranger on the other side of the world.
- escapecharacter 8y agoI’m going to save eigen-face as a pocket insult the next time I need to call someone generic-looking.
- brownbat 8y agoThere has been a lot of research on on the "zoo" of biometrics. Doddington's zoo or the biometric menagerie make reasonable search terms. The idea is that you have a few classes of errors in any calibrated heuristic system, and biometrics fall prey to the same ones. The individuals that trigger these errors were given animal names. Given a large enough population, most of the population will be "sheep" - the system works as intended for these. Some individuals will be prone to false positives. They will pass the system even though they should fail. We'll call these wolves. Others will be prone to false negatives - they will fail even when they should pass. Goats. Lambs are a group that is easy to mimic - so others will be mistaken for them once they are in the system, so they sort of perpetuate wolves by having a really generic set of distinguishing traits. I think this zoo was extended for other unique cases and other animals, but I'm having trouble finding links to the most popular research papers in this series. The research prompting the posted article seems to be about identifying "wolves" - a good demonstration of the validity of Doddington's theories. * Wolves are technically just good mimickers, I'm simplifying a bit above.
- ikeboy 8y agoDo these classifications correlate with any real world observables besides from biometrics?
- animal531 8y agoI imagine to make it to the real world you only need a 3d printer (that can print at sufficiently high density) using a gel substance. After that you can simply lick it and apply.