RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches
  on Face Recognition
Xiaoliang Liu Furao Shen Jian Zhao Changhai Nie
Face recognition (FR) systems powered by deep learning have become widely
used in various applications. However, they are vulnerable to adversarial
attacks, especially those based on local adversarial patches that can be
physically applied to real-world objects. In this paper, we propose RADAP, a
robust and adaptive defense mechanism against diverse adversarial patches in
both closed-set and open-set FR systems. RADAP employs innovative techniques,
such as FCutout and F-patch, which use Fourier space sampling masks to improve
the occlusion robustness of the FR model and the performance of the patch
segmenter. Moreover, we introduce an edge-aware binary cross-entropy (EBCE)
loss function to enhance the accuracy of patch detection. We also present the
split and fill (SAF) strategy, which is designed to counter the vulnerability
of the patch segmenter to complete white-box adaptive attacks. We conduct
comprehensive experiments to validate the effectiveness of RADAP, which shows
significant improvements in defense performance against various adversarial
patches, while maintaining clean accuracy higher than that of the undefended
Vanilla model.