Using "Master Faces" to Bypass Face-Recognition Authenticating Systems
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Fascinating research: “Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution.”
Abstract: 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.
Sidebar photo of Bruce Schneier by Joe MacInnis.
Text extracted automatically; images, tables and formatting may be missing. Original: https://www.schneier.com/blog/archives/2021/08/using-master-faces-to-bypass-face-recognition-authenticating-systems.html