Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness. Several …