DeepMasterPrint: Generating Fingerprints for Presentation Attacks

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% of all users in a strict security setting, and 77% of all users at a looser security setting.