Biometric Face Recognition Exploit

clscott writes "A researcher at the U. of Ottawa has developed an exploit to which most biometric systems are probably vulnerable. He developed an algorithm which allows a fairly high quality image of a person to be regenerated from a face recognition template. Three commercial face rec. algorithms were tested and in all cases the image could masquerade to the algorithm as the target person. Here are links to a talk and a paper. Unfortunately, biometric templates are currently considered to be non-identifiable, much like a password hash. This means that legislation gets passed to require hundreds of millions of people to have their biometrics encoded onto their passports. This kind of vulnerability could mean that anyone who reads these documents has access to the holders fingerprint, iris images, etc."

Comments

Re:Facial recognition

[Herr_Nightingale]

the point that EVERYbody is missing is that biometric authentication is inherently flawed - it's like a password that cannot be changed. Obviously there are innumerable flaws. How is this news?

RTFA yourself

[MarcoAtWork]

You don't understand what the article is talking about. When you enroll in a biometric system, the system itself -doesn't- match based on your picture, but on a 'template' which is created by taking your standard data and performing certain destructive operations to arrive to a much smaller 'template' which can still be used to identify you.

This is very similar to the one-way hashing that happens with unix passwords, only that in this case the hashing is 'lossier' so you have 'confidence scores' instead of a black/white answer.

The article shows that given this 'hashed' value you can recreate an image that has a good chance of not only being authenticated by the same system/algorithm (which already should be very hard, given the one-way nature of the templatization) =BUT= also by different systems!

It also is really interesting how if you have access to the 'confidence score' outputted by the recognizer, you can take arbitrary images and blending/averaging them again come up with an image that works.

This is definitely not useless news and will have quite some implications.

Not as significant as you might think

[swillden]

This isn't such a big deal for face recognition systems, because face recognition systems suck at identifying people anyway. Why? First a little tereminology:

With any biometric matcher you have to define a match "tolerance", which defines how close a pair of templates (usually one from a database and one from a livescan) have to be before they're considered to be a match. Set this tolerance too "loose" and you get lots of false positives (matches that shouldn't match), set it too "tight" and you get the opposite, false negatives. The tolerance setting where you get roughly the same number of errors each way is called the equal error point, and the error rate is called the equal error rate (abbreviated ERR for some unfathomable reason).

Well, all current face recognition systems have an ERR that is too high to be useful in nearly any situation, even when used for identity verification, as opposed to the much-harder problem of identification (verification: I say I'm Bill Gates, and the system agrees; identification: The system says I'm Bill Gates, not RMS or anyone else). It's possible that in the future this will change, of course.

However, this doesn't really matter because we already have ready access to an excellent and very widely available face recognition system: the Mark I eyeball. Millions of years of evolution have made people extremely good at identifying and matching human faces. What people aren't so good at (with notable exceptions) is matching a face against a database of thousands of faces they've seen only once, and *that* is something that face recognition systems can do extremely well. They may not be able to decide which faces are a "match", but they can do an excellent job of finding the *closest* faces, which can then be reviewed by the super-duper face-matching algorithm contained in the average person's head.

When automated face recognition is used in that sort of context, spoofs like this one are unlikely to be very useful; if you want to impersonate someone you'd better get a face that's good enough to fool another human. It's doable, certainly, but much harder. And holding a laptop screen in front of your face is likely to raise some suspicions.