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DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
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.
Previous work by Roy et al. generated synthetic MasterPrints at the
feature-level. In this work we generate complete image-level MasterPrints known
as DeepMasterPrints, whose attack accuracy is found to be much superior than
that of previous methods. The proposed method, referred to as Latent Variable
Evolution, is based on training a Generative Adversarial Network on a set of
real fingerprint images. Stochastic search in the form of the Covariance Matrix
Adaptation Evolution Strategy is then used to search for latent input variables
to the generator network that can maximize the number of impostor matches as
assessed by a fingerprint recognizer. Experiments convey the efficacy of the
proposed method in generating DeepMasterPrints. The underlying method is likely
to have broad applications in fingerprint security as well as fingerprint
synthesis.Comment: 8 pages; added new verification systems and diagrams. Accepted to
conference Biometrics: Theory, Applications, and Systems 201
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