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 LFW identities (over 40%) with less than 10 master faces, for three leading deep face recognition systems.

翻译：主脸是一张脸像,通过脸部身份识别,让大部分人口能够通过脸部身份识别。这些面孔可以用来冒充任何用户,而且成功的可能性很大,无法获取任何用户信息。我们优化了这些面孔,在StyleGAN面部生成器的潜在嵌入空间中采用了进化算法。对多种进化战略进行了比较,我们提出了一种新颖的办法,即使用神经网络,引导寻找有希望的样本,而不必增加健康评估。我们介绍的结果表明,有可能为三个领先的深层面部识别系统获得高覆盖率的LFW身份(40%以上),而总面部不到10个。