  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. Besides, our findings also make a
revision of previous inaccurate understanding of the shape bias of
adversarially learned features.
