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Face recognition (FR) systems powered by deep learning have become widelyused in various applications. However, they are vulnerable to adversarialattacks, especially those based on local adversarial patches that can bephysically applied to real-world objects. In this paper, we propose RADAP, arobust and adaptive defense mechanism against diverse adversarial patches inboth closed-set and open-set FR systems. RADAP employs innovative techniques,such as FCutout and F-patch, which use Fourier space sampling masks to improvethe occlusion robustness of the FR model and the performance of the patchsegmenter. Moreover, we introduce an edge-aware binary cross-entropy (EBCE)loss function to enhance the accuracy of patch detection. We also present thesplit and fill (SAF) strategy, which is designed to counter the vulnerabilityof the patch segmenter to complete white-box adaptive attacks. We conductcomprehensive experiments to validate the effectiveness of RADAP, which showssignificant improvements in defense performance against various adversarialpatches, while maintaining clean accuracy higher than that of the undefendedVanilla model.

RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition