  Face recognition is a prevailing authentication solution in numerous
biometric applications. Physical adversarial attacks, as an important
surrogate, can identify the weaknesses of face recognition systems and evaluate
their robustness before deployed. However, most existing physical attacks are
either detectable readily or ineffective against commercial recognition
systems. The goal of this work is to develop a more reliable technique that can
carry out an end-to-end evaluation of adversarial robustness for commercial
systems. It requires that this technique can simultaneously deceive black-box
recognition models and evade defensive mechanisms. To fulfill this, we design
adversarial textured 3D meshes (AT3D) with an elaborate topology on a human
face, which can be 3D-printed and pasted on the attacker's face to evade the
defenses. However, the mesh-based optimization regime calculates gradients in
high-dimensional mesh space, and can be trapped into local optima with
unsatisfactory transferability. To deviate from the mesh-based space, we
propose to perturb the low-dimensional coefficient space based on 3D Morphable
Model, which significantly improves black-box transferability meanwhile
enjoying faster search efficiency and better visual quality. Extensive
experiments in digital and physical scenarios show that our method effectively
explores the security vulnerabilities of multiple popular commercial services,
including three recognition APIs, four anti-spoofing APIs, two prevailing
mobile phones and two automated access control systems.
