Masked Faces with Faced Masks
Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang, Liu, Geguang Pu

TL;DR
This paper investigates the vulnerability of face recognition systems with mask detectors by generating realistic adversarial masked faces that can evade both recognition and detection, revealing security risks.
Contribution
It introduces a novel adversarial face masking method that fools both face recognition and mask detection systems while maintaining face realism.
Findings
Significant degradation of face recognition accuracy.
Effective evasion of mask detection by adversarial masks.
Proposed methods produce more natural-looking adversarial faces.
Abstract
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,…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Face recognition and analysis · Generative Adversarial Networks and Image Synthesis
