14 citations · 17 across the 7 of their papers we have counts for
8 papers
Let the Carrier Carry the Attack: Preserving the Subject in Adversarial Image Generation
Linfeng Jiang, Steven McDonagh, Yuhang Chen +3
Strong unrestricted adversarial attacks can distort the primary object of an image, hereafter referred to as the subject. To preserve subject integrity without compromising attack…
Fragile by Design: On the Limits of Adversarial Defenses in Personalized Generation
Zhen Chen, Yi Zhang, Xiangyu Yin +4
Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of…
TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models
Xiangyu Yin, Yi Qi, Jinwei Hu +5
Vision Language Models (VLMs) have demonstrated impressive inference capabilities, but remain vulnerable to jailbreak attacks that can induce harmful or unethical responses. Existi…
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
Zhenglin Huang, Jinwei Hu, Xiangtai Li +6
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when share…
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Sihao Wu, Jiaxu Liu, Xiangyu Yin +5
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of pur…
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Sihao Wu, Xingyu Zhao, Xiaowei Huang
Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. I…
Building Guardrails for Large Language Models
Yi Dong, Ronghui Mu, Gaojie Jin +6
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts…
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning
Xiangyu Yin, Sihao Wu, Jiaxu Liu +4
While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust r…