A Unified Game-Theoretic Interpretation of Adversarial Robustness

Understand

This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{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.