Towards Interpreting and Utilizing Symmetry Property in Adversarial Examples - Mei, Shibin; Zhao, Chenglong; Ni, Bingbing; Yuan, Shengchao (Proceedings of the AAAI Conference on Artificial Intelligence, 2023)
In this paper, we identify symmetry property in adversarial scenario by viewing adversarial attack in a fine-grained manner. A newly designed metric called attack proportion, is thus proposed to count the proportion of the adversarial examples misclassified between classes. We observe that the distribution of attack proportion is unbalanced as each class shows vulnerability to particular classes. Further, some class pairs correlate strongly and have the same degree of attack proportion for each other. We call this intriguing phenomenon symmetry property. We empirically prove this phenomenon is widespread and then analyze the reason behind the existence of symmetry property. This explanation, to some extent, could be utilized to understand robust models, which also inspires us to strengthen adversarial defenses.