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Although Deep Neural Networks (DNNs) have been widely applied in variousreal-world scenarios, they are vulnerable to adversarial examples. The currentadversarial attacks in computer vision can be divided into digital attacks andphysical attacks according to their different attack forms. Compared withdigital attacks, which generate perturbations in the digital pixels, physicalattacks are more practical in the real world. Owing to the serious securityproblem caused by physically adversarial examples, many works have beenproposed to evaluate the physically adversarial robustness of DNNs in the pastyears. In this paper, we summarize a survey versus the current physicallyadversarial attacks and physically adversarial defenses in computer vision. Toestablish a taxonomy, we organize the current physical attacks from attacktasks, attack forms, and attack methods, respectively. Thus, readers can have asystematic knowledge of this topic from different aspects. For the physicaldefenses, we establish the taxonomy from pre-processing, in-processing, andpost-processing for the DNN models to achieve full coverage of the adversarialdefenses. Based on the above survey, we finally discuss the challenges of thisresearch field and further outlook on the future direction.

Visually Adversarial Attacks and Defenses in the Physical World: A Survey