Non-Lipschitz Attack: A More Sparse Adversarial Attack via Non-Lipschitz $ℓ_p$ Regularization (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 fewtheoretical results in these works. In this paper, we propose a novel sparse attackapproach named the non-Lipschitz attack (NLA). For the proposed $ℓ_p \ (0< p