(untitled) (CSIAM Transactions on Applied Mathematics, 2023)
Deep neural networks are considerably vulnerable to adversarial attacks. Therein, sparse attacks mislead image classifiers with a sparse, pixel-level perturbation that alters few pixels, and have much potential in physical world applications. The existing sparse attacks are mostly based on ℓ_0 optimization, and there are few theoretical results in these works. In this paper, we propose a novel sparse attack approach named the non-Lipschitz attack (NLA). For the proposed ℓ_p (0< p