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 has been widely applied in various fields such as computer vision, natural
language processing, and data mining. Although deep learning has achieved significant …

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 neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

Estimating the risk level of adversarial examples is essential for safely deploying machine
learning models in the real world. One popular approach for physical-world attacks is to …

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial
examples, there is an urgent need for an effective adversarial attack to identify the …

Deep neural networks have achieved unprecedented success on diverse vision tasks.
However, they are vulnerable to adversarial noise that is imperceptible to humans. This …

Vision transformers (ViTs) have been successfully deployed in a variety of computer vision
tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local …

Vision transformers (ViTs) have demonstrated impressive performance on a series of
computer vision tasks, yet they still suffer from adversarial examples. In this paper, we posit …

Although deep neural networks (DNNs) have achieved tremendous performance in diverse
vision challenges, they are surprisingly susceptible to adversarial examples, which are born …