DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution

• Published in: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) pp. 1 - 9
• Main Authors: Bontrager, Philip, Roy, Aditi, Togelius, Julian, Memon, Nasir, Ross, Arun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Covariance matrices; Gallium nitride; Generative adversarial networks; Generators; Neural networks; Optimization; Training
• ISSN: 2474-9699
• DOI: 10.1109/BTAS.2018.8698539

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.