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arXiv · 2103.07364

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren·Die Zhang·Yisen Wang·Lu Chen·Zhanpeng Zhou·Yiting Chen·Xu Cheng·Xin Wang·Meng Zhou·Jie Shi·Quanshi Zhang
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Abstract

This paper provides a unified view to explain different adversarial attacks and defense methods, i.e. the view of multi-order interactions between input variables of DNNs. Based on the multi-order interaction, we discover that adversarial attacks mainly affect high-order interactions to fool the DNN. Furthermore, we find that the robustness of adversarially trained DNNs comes from category-specific low-order interactions. Our findings provide a potential method to unify adversarial perturbations and robustness, which can explain the existing defense methods in a principle way. Besides, our findings also make a revision of previous inaccurate understanding of the shape bias of adversarially learned features.

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Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang. 2021-03-12. A Unified Game-Theoretic Interpretation of Adversarial Robustness. https://arxiv.org/abs/2103.07364

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