DeepMasterPrint: Generating Fingerprints for Presentation Attacks
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We present two related methods for creating MasterPrints, synthetic fingerprints that are capable of spoofing multiple people's fingerprints. These methods achieve results that advance the state-of-the-art for single MasterPrint attack accuracy while being the first methods capable of creating MasterPrints at the image level. Both of the methods presented in this paper start with training a Generative Adversarial Network (GAN) on a set of real fingerprint images. The generator network is then used to search for fingerprints that maximize the probability of matching with most subjects in a dataset. The first method uses evolutionary search in the space of latent variables, and the second method uses gradient-based optimization. The unique combination of evolution and GANs is able to design a MasterPrint that a commercial fingerprint system matches to 23 setting, and 77
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