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 …

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to
adversarial examples. We find, however, that typical adaptive evaluations are incomplete …

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 …

Making classifiers robust to adversarial examples is challenging. Thus, many works tackle
the seemingly easier task of detecting perturbed inputs. We show a barrier towards this goal …

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a
predominant solution within the deep learning landscape. Model providers furnish pre …

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 …

When compared to the image classification models, black-box adversarial attacks against
video classification models have been largely understudied. This could be possible …

Recent advances in artificial intelligence and the increasing need for robust defensive
measures in network security have led to the adoption of deep learning approaches for …

In many non-stationary environments, machine learning algorithms usually confront the
distribution shift scenarios. Previous domain adaptation methods have achieved great …

Widely deployed deep neural network (DNN) models have been proven to be vulnerable to
adversarial perturbations in many applications (eg, image, audio and text classifications). To …