In the race of arms between attackers, trying to build more and more realistic face replay attacks, and defenders, deploying spoof detection modules with ever-increasing capabilities, CNN-based methods have shown outstanding detection performance thus raising the bar for the construction of realistic replay attacks against face-based authentication systems. Rather than trying to rebroadcast even more realistic faces, we show that attackers can successfully fool a face authentication system equipped with a deep learning spoof detection module, by exploiting the vulnerabilities of CNNs to adversarial perturbations. We first show that mounting such an attack is not a trivial task due to the unique features of spoofing detection modules. Then, we propose a method to craft adversarial images that can be successfully exploited to build an effective replay attack. Experiments conducted on the REPLAY-MOBILE database demonstrate that our attacked images achieve good performance against a face recognition system equipped with CNN-based anti-spoofing, in that they are able to pass the face detection, spoof detection and face recognition modules of the authentication chain.

在攻击者之间的军备竞赛中，试图建立越来越多的逼真的面部重播攻击，而防御者则通过部署具有不断增强的功能的欺骗检测模块，基于CNN的方法表现出了出色的检测性能，从而为构建逼真的构造树立了标杆。重播针对基于面部的身份验证系统的攻击。与其试图重播甚至更真实的面孔，我们还展示了攻击者可以通过利用CNN的对抗性扰动，成功地欺骗配备了深度学习欺骗检测模块的面孔认证系统。我们首先表明，由于欺骗检测模块的独特功能，发起这样的攻击并非易事。然后，我们提出一种制作对抗图像的方法，可以成功利用该图像来构建有效的重放攻击。在REPLAY-MOBILE数据库上进行的实验表明，针对带有基于CNN的反欺骗的面部识别系统，我们的被攻击图像能够通过身份验证的面部检测，欺骗检测和面部识别模块，从而取得了良好的性能链。