Adversarial training is one of the most effective approaches defending against adversarial
examples for deep learning models. Unlike other defense strategies, adversarial training …

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

It is common practice in deep learning to use overparameterized networks and train for as
long as possible; there are numerous studies that show, both theoretically and empirically …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

A recent study (Rice et al., 2020) revealed overfitting to be a dominant phenomenon in
adversarially robust training of deep networks, and that appropriate early-stopping of …

A key challenge in adversarial robustness is the lack of a precise mathematical
characterization of human perception, used in the very definition of adversarial attacks that …

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …

Adversarial training yields robust models against a specific threat model, eg, $ L_\infty $
adversarial examples. Typically robustness does not generalize to previously unseen threat …