Understanding how to defend against adversarial attacks is crucial for ensuring the safety and reliability of these systems in real-world applications. Various adversarial defense methods are proposed, which aim at improving the robustness of neural ...

Due to the wide deployment of deep learning applications in safety-critical systems, robust and secure execution of deep learning workloads is imperative. Adversarial examples, where the inputs are carefully designed to mislead the machine learning ...