Adversarial training is a standard technique for training adversarially robust models. In this paper, we study adversarial training as an alternating best-response strategy in a 2-player zero-sum game. We prove that even in a simple scenario of a linear classifier and a statistical model that abstracts robust vs. non-robust features, the alternating best response strategy of such game may not converge. On the other hand, a unique pure Nash equilibrium of the game exists and is provably robust. We support our theoretical results with experiments, showing the non-convergence of adversarial training and the robustness of Nash equilibrium.
@article{arxiv.2210.12606,
title = {Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games},
author = {Maria-Florina Balcan and Rattana Pukdee and Pradeep Ravikumar and Hongyang Zhang},
journal= {arXiv preprint arXiv:2210.12606},
year = {2023}
}