Stealthy Physical Masked Face Recognition Attack via Adversarial Style
  Optimization
Huihui Gong Minjing Dong Siqi Ma Seyit Camtepe Surya Nepal Chang Xu
Deep neural networks (DNNs) have achieved state-of-the-art performance on
face recognition (FR) tasks in the last decade. In real scenarios, the
deployment of DNNs requires taking various face accessories into consideration,
like glasses, hats, and masks. In the COVID-19 pandemic era, wearing face masks
is one of the most effective ways to defend against the novel coronavirus.
However, DNNs are known to be vulnerable to adversarial examples with a small
but elaborated perturbation. Thus, a facial mask with adversarial perturbations
may pose a great threat to the widely used deep learning-based FR models. In
this paper, we consider a challenging adversarial setting: targeted attack
against FR models. We propose a new stealthy physical masked FR attack via
adversarial style optimization. Specifically, we train an adversarial style
mask generator that hides adversarial perturbations inside style masks.
Moreover, to ameliorate the phenomenon of sub-optimization with one fixed
style, we propose to discover the optimal style given a target through style
optimization in a continuous relaxation manner. We simultaneously optimize the
generator and the style selection for generating strong and stealthy
adversarial style masks. We evaluated the effectiveness and transferability of
our proposed method via extensive white-box and black-box digital experiments.
Furthermore, we also conducted physical attack experiments against local FR
models and online platforms.