4 citations · 14 across the 14 of their papers we have counts for
9 papers · 1 filter
Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
Yuchen Chen, Weisong Sun, Haocheng Huang +11
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical…
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs
Peizhuo Lv, Yiran Xiahou, Congyi Li +4
LoRA (Low-Rank Adaptation) has achieved remarkable success in the parameter-efficient fine-tuning of large models. The trained LoRA matrix can be integrated with the base model thr…
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models
Peizhuo Lv, Mengjie Sun, Hao Wang +5
In recent years, tremendous success has been witnessed in Retrieval-Augmented Generation (RAG), widely used to enhance Large Language Models (LLMs) in domain-specific, knowledge-in…
PersonaMark: Personalized LLM watermarking for model protection and user attribution
Yuehan Zhang, Peizhuo Lv, Yinpeng Liu +5
The rapid advancement of customized Large Language Models (LLMs) offers considerable convenience. However, it also intensifies concerns regarding the protection of copyright/confid…
MEA-Defender: A Robust Watermark against Model Extraction Attack
Peizhuo Lv, Hualong Ma, Kai Chen +6
Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners ov…
DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models
Jiachen Zhou, Peizhuo Lv, Yibing Lan +3
Dataset sanitization is a widely adopted proactive defense against poisoning-based backdoor attacks, aimed at filtering out and removing poisoned samples from training datasets. Ho…