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 [BibTeX] [Marc21]
Generating Master Faces for Use in PerformingWolf Attacks on Face Recognition Systems
Type of publication: Conference paper
Citation: Nguyen_IJCB_2020
Publication status: Accepted
Booktitle: International Join Conference on Biometrics
Year: 2020
Month: September
Abstract: Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown that finger-vein- and fingerprint-based authentication methods are susceptible to wolf attacks, in which a wolf sample matches many enrolled user templates. In this work, we demonstrated that wolf (generic) faces, which we call “master faces,” can also compromise face recognition systems and that the master face concept can be generalized in some cases. Motivated by recent similar work in the fingerprint domain, we generated high-quality master faces by using the state-of-the-art face generator StyleGAN in a process called latent variable evolution. Experiments demonstrated that even attackers with limited resources using only pre-trained models available on the Internet can initiate master face attacks. The results, in addition to demonstrating performance from the attacker’s point of view, can also be used to clarify and improve the performance of face recognition systems and harden face authentication systems.
Keywords: Anti-spoofing, deep learning, Deepfakes, Face Presentation Attack Detection, Face Recognition, GANs
Projects Idiap
Authors Nguyen, Huy H.
Yamagishi, Junichi
Echizen, Isao
Marcel, Sébastien
Added by: [UNK]
Total mark: 0
  • Nguyen_IJCB_2020.pdf