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 …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

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 …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

Deep neural networks have demonstrated excellent performance in most computer vision
tasks in recent years. However, they are vulnerable to adversarial perturbations generated …

An unresolved problem in Deep Learning is the ability of neural networks to cope with
domain shifts during test-time, imposed by commonly fixing network parameters after …

Abstract Deep Neural Networks show superior performance in various tasks but are
vulnerable to adversarial attacks. Most defense techniques are devoted to the adversarial …

Recent studies in deep neural networks have shown that injecting random noise in the input
layer of the networks contributes towards ℓ p-norm-bounded adversarial perturbations …

Targeted attack aims to mislead the classification model to a specific class, and it can be
further divided into black-box and white-box targeted attack depending on whether the …

Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for
improving the robustness of the deep neural networks. However, PGD AT has been shown …