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 …

The field of defense strategies against adversarial attacks has significantly grown over the
last years, but progress is hampered as the evaluation of adversarial defenses is often …

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 …

Previous adversarial training raises model robustness under the compromise of accuracy on
natural data. In this paper, we reduce natural accuracy degradation. We use the model logits …

Training classifiers under fairness constraints such as group fairness, regularizes the
disparities of predictions between the groups. Nevertheless, even though the constraints are …

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which
is attracting increasing attention because it promises to bring vast benefits for consumers …

Deep neural networks are susceptible to adversarial examples, posing a significant security
risk in critical applications. Adversarial Training (AT) is a well-established technique to …

The early phase of training a deep neural network has a dramatic effect on the local
curvature of the loss function. For instance, using a small learning rate does not guarantee …

The problem of adversarial defenses for image classification, where the goal is to robustify a
classifier against adversarial examples, is considered. Inspired by the hypothesis that these …