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A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly
Yifan Yao, Jinhao Duan, Kaidi Xu +3
Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text…
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…
Pursing the Sparse Limitation of Spiking Deep Learning Structures
Hao Cheng, Jiahang Cao, Erjia Xiao +7
Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial…
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models
Fei Kong, Jinhao Duan, Lichao Sun +6
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds. Consistency training addresses this issue with single-step sampling…
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Hao Cheng, Jinhao Duan, Hui Li +6
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP). Compared with…
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…