Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability

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.