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 …

Chain-of-thought prompting (CoT) advances the reasoning abilities of large language
models (LLMs) and achieves superior performance in arithmetic, commonsense, and …

Classic black-box adversarial attacks can take advantage of transferable adversarial
examples generated by a similar substitute model to successfully fool the target model …

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

The increasing scale of general-purpose Pre-trained Language Models (PLMs) necessitates
the study of more efficient adaptation across different downstream tasks. In this paper, we …

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 …

The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great
attention from the community. In this paper, we study the transferability of such examples …

Transfer-based attack adopts the adversarial examples generated on the surrogate model to
attack various models, making it applicable in the physical world and attracting increasing …