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The physical attack has been regarded as a kind of threat against real-worldcomputer vision systems. Still, many existing defense methods are only usefulfor small perturbations attacks and can't detect physical attacks effectively.In this paper, we propose a random-patch based defense strategy to robustlydetect physical attacks for Face Recognition System (FRS). Different frommainstream defense methods which focus on building complex deep neural networks(DNN) to achieve high recognition rate on attacks, we introduce a patch baseddefense strategy to a standard DNN aiming to obtain robust detection models.Extensive experimental results on the employed datasets show the superiority ofthe proposed defense method on detecting white-box attacks and adaptive attackswhich attack both FRS and the defense method. Additionally, due to thesimpleness yet robustness of our method, it can be easily applied to the realworld face recognition system and extended to other defense methods to boostthe detection performance.

A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems