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Due to its convenience, biometric authentication, especial faceauthentication, has become increasingly mainstream and thus is now a primetarget for attackers. Presentation attacks and face morphing are typical typesof attack. Previous research has shown that finger-vein- and fingerprint-basedauthentication methods are susceptible to wolf attacks, in which a wolf samplematches many enrolled user templates. In this work, we demonstrated that wolf(generic) faces, which we call "master faces," can also compromise facerecognition systems and that the master face concept can be generalized in somecases. Motivated by recent similar work in the fingerprint domain, we generatedhigh-quality master faces by using the state-of-the-art face generator StyleGANin a process called latent variable evolution. Experiments demonstrated thateven attackers with limited resources using only pre-trained models availableon the Internet can initiate master face attacks. The results, in addition todemonstrating performance from the attacker's point of view, can also be usedto clarify and improve the performance of face recognition systems and hardenface authentication systems.

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems