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arXiv · 2111.13613

The Geometry of Adversarial Training in Binary Classification

Leon Bungert·Nicolás García Trillos·Ryan Murray
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Abstract

We establish an equivalence between a family of adversarial training problems for non-parametric binary classification and a family of regularized risk minimization problems where the regularizer is a nonlocal perimeter functional. The resulting regularized risk minimization problems admit exact convex relaxations of the type $L^1+$ (nonlocal) $\operatorname{TV}$, a form frequently studied in image analysis and graph-based learning. A rich geometric structure is revealed by this reformulation which in turn allows us to establish a series of properties of optimal solutions of the original problem, including the existence of minimal and maximal solutions (interpreted in a suitable sense), and the existence of regular solutions (also interpreted in a suitable sense). In addition, we highlight how the connection between adversarial training and perimeter minimization problems provides a novel, directly interpretable, statistical motivation for a family of regularized risk minimization problems involving perimeter/total variation. The majority of our theoretical results are independent of the distance used to define adversarial attacks.

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BibTeXRIS

Leon Bungert, Nicolás García Trillos, Ryan Murray. 2021-11-26. The Geometry of Adversarial Training in Binary Classification. https://doi.org/10.1093/imaiai%2Fiaac029

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