  Face recognition has achieved considerable progress in recent years thanks to
the development of deep neural networks, but it has recently been discovered
that deep neural networks are vulnerable to adversarial examples. This means
that face recognition models or systems based on deep neural networks are also
susceptible to adversarial examples. However, the existing methods of attacking
face recognition models or systems with adversarial examples can effectively
complete white-box attacks but not black-box impersonation attacks, physical
attacks, or convenient attacks, particularly on commercial face recognition
systems. In this paper, we propose a new method to attack face recognition
models or systems called RSTAM, which enables an effective black-box
impersonation attack using an adversarial mask printed by a mobile and compact
printer. First, RSTAM enhances the transferability of the adversarial masks
through our proposed random similarity transformation strategy. Furthermore, we
propose a random meta-optimization strategy for ensembling several pre-trained
face models to generate more general adversarial masks. Finally, we conduct
experiments on the CelebA-HQ, LFW, Makeup Transfer (MT), and CASIA-FaceV5
datasets. The performance of the attacks is also evaluated on state-of-the-art
commercial face recognition systems: Face++, Baidu, Aliyun, Tencent, and
Microsoft. Extensive experiments show that RSTAM can effectively perform
black-box impersonation attacks on face recognition models or systems.
