1 citations · 3 across the 6 of their papers we have counts for
6 papers
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models
Youze Wang, Wenbo Hu, Yinpeng Dong +3
Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other type…
Natural Language Induced Adversarial Images
Xiaopei Zhu, Peiyang Xu, Guanning Zeng +2
Research of adversarial attacks is important for AI security because it shows the vulnerability of deep learning models and helps to build more robust models. Adversarial attacks o…
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
Qian Li, Yuxiao Hu, Yinpeng Dong +2
Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i…
Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning
Youze Wang, Wenbo Hu, Yinpeng Dong +3
The integration of visual and textual data in Vision-Language Pre-training (VLP) models is crucial for enhancing vision-language understanding. However, the adversarial robustness…
The art of defense: letting networks fool the attacker
Jinlai Zhang, Yinpeng Dong, Binbin Liu +5
Robust environment perception is critical for autonomous cars, and adversarial defenses are the most effective and widely studied ways to improve the robustness of environment perc…