Deep Neural Networks (DNNs) have demonstrated remarkable success in various domains but remain susceptible to adversarial examples: slightly altered inputs designed to induce misclassification. While adversarial attacks typically optimize under \(L_p\) -norm constraints, attacks based on the \(L_0\) -norm, which prioritize input sparsity, are less studied due to their complex, 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 \(L_0\) -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 of the \(L_0\) norm, aimed at refining the robustness evaluation of DNNs against such perturbations.