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 …

Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

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 …

The human visual system is remarkably robust against a wide range of naturally occurring
variations and corruptions like rain or snow. In contrast, the performance of modern image …

Standard adversarial attacks change the predicted class label of a selected image by adding
specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an …

The intriguing phenomenon of adversarial examples has attracted significant attention in
machine learning and what might be more surprising to the community is the existence of …

Existing works have identified the limitation of top-1 attack success rate (ASR) as a metric to
evaluate the attack strength but exclusively investigated it in the white-box setting, while our …

Deep learning has gained tremendous success and great popularity in the past few years.
However, deep learning systems are suffering several inherent weaknesses, which can …

Over the past decade, Deep Learning has emerged as a useful and efficient tool to solve a
wide variety of complex learning problems ranging from image classification to human pose …

Abstract The reliability of Deep Learning systems depends on their accuracy but also on
their robustness against adversarial perturbations to the input data. Several attacks and …