Benefiting from the rapid development of deep learning, 2D and 3D computer vision
applications are deployed in many safe-critical systems, such as autopilot and identity …

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks,
yet they are under the risk of adversarial examples that can be easily generated when the …

Decision-based black-box attacks often necessitate a large number of queries to craft an
adversarial example. Moreover, decision-based attacks based on querying boundary points …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

It is a challenging task to fool an image classifier based on deep neural networks under the
black-box setting where the target model can only be queried. Among existing black-box …

Deep neural networks are vulnerable to adversarial attacks. We study such threats in the
decision-based black-box setting where the adversary could obtain only the predicted labels …

While Large Language Models (LLMs) have achieved tremendous success in various
applications, they are also susceptible to jailbreaking attacks. Several primary defense …

Autonomous vehicles use deep neural networks (DNNs) to build powerful vision perception
systems, which provide a theoretical foundation for automated vehicle control. Due to the …

Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and
enhancing their adversarial robustness. Among various attack types, hard-label attacks that …

Deep neural networks are vulnerable to adversarial examples, where decision-based
attacks can generate adversarial examples based solely on the predicted labels. However …