24 citations · 56 across the 11 of their papers we have counts for
6 papers · 1 filter
CNT: Safety-oriented Function Reuse across LLMs via Cross-Model Neuron Transfer
Yue Zhao, Yujia Gong, Ruigang Liang +4
The widespread deployment of large language models (LLMs) calls for post-hoc methods that can flexibly adapt models to evolving safety requirements. Meanwhile, the rapidly expandin…
Turn-Based Structural Triggers: Structure-Conditioned Backdoors in Multi-Turn LLMs
Yiyang Lu, Jinwen He, Yue Zhao +4
Large Language Models (LLMs) are increasingly deployed as multi-turn assistants and customized through instruction tuning with project-specific training components. This practice c…
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…
SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning
Peizhuo Lv, Pan Li, Shenchen Zhu +9
Recent years have witnessed tremendous success in Self-Supervised Learning (SSL), which has been widely utilized to facilitate various downstream tasks in Computer Vision (CV) and…
Semantics-Recovering Decompilation through Neural Machine Translation
Ruigang Liang, Ying Cao, Peiwei Hu +2
Decompilation transforms low-level program languages (PL) (e.g., binary code) into high-level PLs (e.g., C/C++). It has been widely used when analysts perform security analysis on…
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks
Peizhuo Lv, Pan Li, Shengzhi Zhang +4
Due to the wide use of highly-valuable and large-scale deep neural networks (DNNs), it becomes crucial to protect the intellectual property of DNNs so that the ownership of dispute…