Adversarial training (AT) has become an effective defense method against adversarial
examples (AEs) and it is typically framed as a bi-level optimization problem. Among various …

Adversarial training (AT) refers to integrating adversarial examples--inputs altered with
imperceptible perturbations that can significantly impact model predictions--into the training …

Catastrophic overfitting (CO) poses a significant challenge to fast adversarial training
(FastAT), particularly at large perturbation scales, leading to dramatic reductions in …

Catastrophic overfitting (CO) presents a significant challenge in single-step adversarial
training (AT), manifesting as highly distorted deep neural networks (DNNs) that are …

Models resistant to adversarial perturbations are stable around the neighbourhoods of input
images, such that small changes, known as adversarial attacks, cannot dramatically change …

The widespread adoption of deep neural networks (DNNs) has raised significant concerns
about their robustness, particularly in real-world environments characterized by inherent …