3 citations · 5 across the 6 of their papers we have counts for
6 papers
Gecko: Fast Private Inference via Secure Public Encoder Offloading
Cheng'an Wei, Kai Chen, Yue Zhao +2
Private inference protects both user inputs and server models during neural network inference, but existing solutions remain too slow for practical deployment. This motivates recen…
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…
Hidden in Plain Sight: Exploring Chat History Tampering in Interactive Language Models
Cheng'an Wei, Yue Zhao, Yujia Gong +3
Large Language Models (LLMs) such as ChatGPT and Llama have become prevalent in real-world applications, exhibiting impressive text generation performance. LLMs are fundamentally d…
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…
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information
Pan Li, Peizhuo Lv, Shenchen Zhu +2
Unlike traditional static deep neural networks (DNNs), dynamic neural networks (NNs) adjust their structures or parameters to different inputs to guarantee accuracy and computation…
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…