Deep Neural Networks have demonstrated remarkable success in various domains but remain susceptible to adversarial examples, which are slightly altered inputs designed to induce misclassification. While adversarial attacks typically optimize under Lp norm constraints, attacks based on the L0 norm, prioritising input sparsity, are less studied due to their complex and non convex nature. These sparse adversarial examples challenge existing defenses by altering a minimal subset of features, potentially uncovering more subtle DNN weaknesses. However, the current L0 norm attack methodologies face a trade off between accuracy and efficiency either precise but computationally intense or expedient but imprecise. This paper proposes a novel, scalable, and effective approach to generate adversarial examples based on the L0 norm, aimed at refining the robustness evaluation of DNNs against such perturbations.

该研究解决了在对抗攻击中对深度神经网络（DNN）模型的鲁棒性评估的问题，尤其是利用L0范数生成对抗样本的挑战。提出了一种新颖、可扩展且有效的方法，能够通过最少特征调整生成对抗样本，从而更好地评估DNN对这些扰动的防御能力。研究表明，这种方法能够更精准地揭示DNN的潜在弱点，提升鲁棒性评估的准确性。

基于自适应稀疏L0正则化评估模型鲁棒性