Generative adversarial network produces a "universal fingerprint" that will unlock many smartphones / Boing Boing
Researchers at NYU and U Michigan have published a paper explaining how they used a pair of machine-learning systems to develop a "universal fingerprint" that can fool the lowest-security fingerprint sensors 76% of the time (it is less effective against higher-security sensors). The researchers used "generative adversarial networks" (GAN) to develop their attack. It is a technology I came across while researching for [Ganbreeder](https://steemhunt.com/@vimukthi/ganbreeder-otherworldly-artwork-generated-through-machine-learning)which generate artwork using Machine Learning.
Smartphones generally operate at the second tier of security, in which they are expected to generate false positives 0.1% of the time; and at this level, the researchers were able to spoof the sensors 22% of the time.
Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems.
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