Researchers created fake "master" fingerprints to unlock smartphones

Donna Miller
November 18, 2018

The researchers built on the previous development by three of their team of MasterPrints, which are images generated from common fingerprint features. They are called MasterPrints.

A team of American academic researchers has developed a neural network to generate artificial fingerprints which can produce false matches on rolled and capacitive biometric verification systems. They trained a generative adversarial network using a dataset of real prints.

The process started with feeding fingerprints from 6,000 people into a neural network in order to train it on what a human fingerprint looks like. They do the same with artificially generated images so that it understands the difference between the two. These prints were then fed into another neural network, a "discriminator", which is created to classify the fake print as real or generated, thus improving their authenticity through trial and error.

Stochastic search in the form of the covariance Matrix Adaptation Evolution Strategy is thenused to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer. The capacitive fingerprints produced better results.

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The paper suggests this technique could be used to create replicated fingerprints that could be used in something akin to a "dictionary attack", but instead of software that runs millions of popular passwords through a system, a DeepMasterPrints-inspired tool could run several fake fingerprints through a system to see if any prints match any accounts. This is the percentage of incorrect fingerprints that it would approve.

These fingerprints, named "DeepMasterPrints" allegedly have an error rate of just one in a thousand. The researchers believe that engineers will have to implement new algorithms and hardware features to combat similar master fingerprint attacks.

The researchers said that the LVE method seemed to be producing partial fingerprint images that contain enough common characteristics to fool fingerprint readers at rates far higher than average.

This is all a little worrying, if someone is able to spoof your fingerprints, then they don't have to steal them (and if they do, you can't upgrade or change your fingerprints).

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