  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, which might trigger
a serious risk, e.g., letting a suspect evade the FRS where both facial
identity and mask are undetected. As existing face recognizers and mask
detectors have high performance in their respective tasks, it is significantly
challenging to simultaneously fool them and preserve the transferability of the
attack. We formulate the new task as the generation of realistic &
adversarial-faced mask and make three main contributions: First, we study the
naive Delanunay-based masking method (DM) to simulate the process of wearing a
faced mask that is cropped from a template image, which reveals the main
challenges of this new task. Second, we further equip the DM with the
adversarial noise attack and propose the adversarial noise Delaunay-based
masking method (AdvNoise-DM) that can fool the face recognition and mask
detection effectively but make the face less natural. Third, we propose the
adversarial filtering Delaunay-based masking method denoted as MF2M by
employing the adversarial filtering for AdvNoise-DM and obtain more natural
faces. With the above efforts, the final version not only leads to significant
performance deterioration of the state-of-the-art (SOTA) deep learning-based
FRS, but also remains undetected by the SOTA facial mask detector, thus
successfully fooling both systems at the same time.
