  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.
