The Many Faces of Adversarial Risk

Adversarial risk quantifies the performance of classifiers on adversarially perturbed data. Numerous definitions of adversarial risk—not all mathematically rigorous and differing subtly in the details—have appeared in the literature. In this paper, we revisit these definitions, fix measure theoretic issues, and critically examine their similarities and differences. Our technical tools derive from optimal transport, robust statistics, functional analysis, and game theory. Our contributions include the following: generalizing Strassen’s theorem to the unbalanced optimal transport setting with applications to adversarial classification with unequal priors; showing an equivalence between adversarial robustness and robust hypothesis testing with <inline-formula> <tex-math notation="LaTeX">$\infty$ </tex-math></inline-formula>-Wasserstein uncertainty sets; proving the existence of a pure Nash equilibrium in the two-player game between the adversary and the algorithm; and characterizing adversarial risk by the minimum Bayes error between a pair of distributions belonging to the <inline-formula> <tex-math notation="LaTeX">$\infty$ </tex-math></inline-formula>-Wasserstein uncertainty sets. Our results generalize and deepen recently discovered connections between optimal transport and adversarial robustness and reveal new connections to Choquet capacities and game theory.