Masked Faces with Faced Masks

Modern face recognition systems (FRS) still fall short when the subjects are wearing facial masks, a common theme in the age of respiratory pandemics. An intuitive partial remedy is to add a mask detector to flag any masked faces so that the FRS can act accordingly for those low-confidence masked faces. In this work, we set out to investigate the potential vulnerability of such FRS, equipped with a mask detector, on large-scale masked faces. As existing face recognizers and mask detectors have high performance in their respective tasks, it is a challenge to simultaneously fool them and preserve the transferability of the attack. To this end, we devise realistic facial masks that exhibit partial face patterns (i.e., faced masks) and stealthily add adversarial textures that can not only lead to significant performance deterioration of the SOTA deep learning-based FRS, but also remain undetected by the SOTA facial mask detector, thus successfully fooling both systems at the same time. The proposed method unveils the vulnerability of the FRS when dealing with masked faces wearing faced masks.