Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution. (arXiv:2108.01077v1 [cs.CR])

A master face is a face image that passes face-based identity-authentication
for a large portion of the population. These faces can be used to impersonate,
with a high probability of success, any user, without having access to any user
information. We optimize these faces, by using an evolutionary algorithm in the
latent embedding space of the StyleGAN face generator. Multiple evolutionary
strategies are compared, and we propose a novel approach that employs a neural
network in order to direct the search in the direction of promising samples,
without adding fitness evaluations. The results we present demonstrate that it
is possible to obtain a high coverage of the population (over 40%) with less
than 10 master faces, for three leading deep face recognition systems.



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