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.

中文翻译：

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使用网络辅助的潜在空间演化为字典攻击生成主人脸

主人脸是通过基于人脸的身份验证对大部分人口进行的人脸图像。这些面孔可用于冒充任何用户，并且成功的可能性很高，而无需访问任何用户信息。我们通过在 StyleGAN 人脸生成器的潜在嵌入空间中使用进化算法来优化这些人脸。比较了多种进化策略，我们提出了一种采用神经网络的新方法，以便将搜索引导到有希望的样本的方向上，而无需添加适应度评估。我们提供的结果表明，对于三个领先的深度人脸识别系统，可以在少于 10 个主人脸的情况下获得高覆盖率（超过 40%）。