10 citations · 27 across the 8 of their papers we have counts for
8 papers
GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing
Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang +12
Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LL…
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning
Chenan Wang, Pu Zhao, Siyue Wang +1
Deep Neural Network (DNN) models when implemented on executing devices as the inference engines are susceptible to Fault Injection Attacks (FIAs) that manipulate model parameters t…
Dynamic Adversarial Attacks on Autonomous Driving Systems
Amirhosein Chahe, Chenan Wang, Abhishek Jeyapratap +2
This paper introduces an attacking mechanism to challenge the resilience of autonomous driving systems. Specifically, we manipulate the decision-making processes of an autonomous v…
Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?
Zhengyue Zhao, Jinhao Duan, Kaidi Xu +5
Stable Diffusion has established itself as a foundation model in generative AI artistic applications, receiving widespread research and application. Some recent fine-tuning methods…
Semantic Adversarial Attacks via Diffusion Models
Chenan Wang, Jinhao Duan, Chaowei Xiao +3
Traditional adversarial attacks concentrate on manipulating clean examples in the pixel space by adding adversarial perturbations. By contrast, semantic adversarial attacks focus o…
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
Jinhao Duan, Hao Cheng, Shiqi Wang +5
Large Language Models (LLMs) show promising results in language generation and instruction following but frequently "hallucinate", making their outputs less reliable. Despite Uncer…
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation
Zhengyue Zhao, Jinhao Duan, Xing Hu +6
Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can…
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations
Hao Cheng, Kaidi Xu, Chenan Wang +3
To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximi…