4 papers
Learning to Transform Dynamically for Better Adversarial Transferability
Rongyi Zhu, Zeliang Zhang, Susan Liang +2
Adversarial examples, crafted by adding perturbations imperceptible to humans, can deceive neural networks. Recent studies identify the adversarial transferability across various m…
Forward Learning for Gradient-based Black-box Saliency Map Generation
Zeliang Zhang, Mingqian Feng, Jinyang Jiang +3
Gradient-based saliency maps are widely used to explain deep neural network decisions. However, as models become deeper and more black-box, such as in closed-source APIs like ChatG…
Bag of Tricks to Boost Adversarial Transferability
Zeliang Zhang, Wei Yao, Xiaosen Wang
Deep neural networks are widely known to be vulnerable to adversarial examples. However, vanilla adversarial examples generated under the white-box setting often exhibit low transf…
Video Understanding with Large Language Models: A Survey
Yolo Y. Tang, Jing Bi, Siting Xu +17
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly. Given…