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 …

Adversarial attacks are valuable for evaluating the robustness of deep learning models.
Existing attacks are primarily conducted on the visible light spectrum (eg, pixel-wise texture …

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 …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

By adding human-imperceptible noise to clean images, the resultant adversarial examples
can fool other unknown models. Features of a pixel extracted by deep neural networks …

Adversarial training has been demonstrated to be one of the most effective remedies for
defending adversarial examples, yet it often suffers from the huge robustness generalization …

Adversarial examples are inputs with imperceptible perturbations that easily misleading
deep neural networks (DNNs). Recently, adversarial patch, with noise confined to a small …

Physical world adversarial attack is a highly practical and threatening attack, which fools real
world deep learning systems by generating conspicuous and maliciously crafted real world …

This work studies the sparse adversarial attack, which aims to generate adversarial
perturbations onto partial positions of one benign image, such that the perturbed image is …