  This paper aims to understand adversarial attacks and defense from a new
perspecitve, i.e., the signal-processing behavior of DNNs. We novelly define
the multi-order interaction in game theory, which satisfies six properties.
With 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 more insights into and make a revision of
previous understanding for the shape bias of adversarially learned features.
Besides, the multi-order interaction can also explain the recoverability of
adversarial examples.
