Traditional adversarial training, while effective at improving machine learning model
robustness, is computationally intensive. Fast Adversarial Training (FAT) addresses this by …

Recent research has shown the vulnerability of deep networks to adversarial perturbations.
Adversarial training and its variants have been shown to be effective defense algorithms …

Adversarial training methods commonly generate independent initial perturbation for
adversarial samples from a simple uniform distribution, and obtain the training batch for the …

Fast Adversarial Training (FAT) has gained increasing attention within the research
community owing to its efficacy in improving adversarial robustness. Particularly noteworthy …

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