12 papers
ComMark: Covert and Robust Black-Box Model Watermarking with Compressed Samples
Yunfei Yang, Xiaojun Chen, Zhendong Zhao +3
The rapid advancement of deep learning has turned models into highly valuable assets due to their reliance on massive data and costly training processes. However, these models are…
Value-Aligned Prompt Moderation via Zero-Shot Agentic Rewriting for Safe Image Generation
Xin Zhao, Xiaojun Chen, Bingshan Liu +3
Generative vision-language models like Stable Diffusion demonstrate remarkable capabilities in creative media synthesis, but they also pose substantial risks of producing unsafe, o…
DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks
Yunfei Yang, Xiaojun Chen, Yuexin Xuan +3
Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific input-output pairs. However, existing wate…
Who Speaks for the Trigger? Dynamic Expert Routing in Backdoored Mixture-of-Experts Transformers
Xin Zhao, Xiaojun Chen, Bingshan Liu +3
Large language models (LLMs) with Mixture-of-Experts (MoE) architectures achieve impressive performance and efficiency by dynamically routing inputs to specialized subnetworks, kno…
AILoRA: Function-Aware Asymmetric Initialization for Low-Rank Adaptation of Large Language Models
Xiaoshuang Ji, Zhendong Zhao, Xiaoyan Gu +3
Parameter-efficient finetuning (PEFT) aims to mitigate the substantial computational and memory overhead involved in adapting large-scale pretrained models to diverse downstream ta…
Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models
Xiaoshuang Ji, Zhendong Zhao, Xiaojun Chen +2
Fine-tuning is a promising technique for leveraging Transformer-based language models in downstream tasks. As model sizes continue to grow, updating all model parameters becomes in…
Model-Guardian: Protecting against Data-Free Model Stealing Using Gradient Representations and Deceptive Predictions
Yunfei Yang, Xiaojun Chen, Yuexin Xuan +1
Model stealing attack is increasingly threatening the confidentiality of machine learning models deployed in the cloud. Recent studies reveal that adversaries can exploit data synt…
Buster: Implanting Semantic Backdoor into Text Encoder to Mitigate NSFW Content Generation
Xin Zhao, Xiaojun Chen, Yuexin Xuan +4
The rise of deep learning models in the digital era has raised substantial concerns regarding the generation of Not-Safe-for-Work (NSFW) content. Existing defense methods primarily…
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling
Xin Zhao, Xiaojun Chen, Xudong Chen +3
Diffusion Models (DMs) achieve state-of-the-art synthesis results in image generation and have been applied to various fields. However, DMs sometimes seriously violate user privacy…
Federated Learning with Limited Node Labels
Bisheng Tang, Xiaojun Chen, Shaopu Wang +2
Subgraph federated learning (SFL) is a research methodology that has gained significant attention for its potential to handle distributed graph-structured data. In SFL, the local m…
Practical and General Backdoor Attacks against Vertical Federated Learning
Yuexin Xuan, Xiaojun Chen, Zhendong Zhao +2
Federated learning (FL), which aims to facilitate data collaboration across multiple organizations without exposing data privacy, encounters potential security risks. One serious t…
A Split-Merge Framework for Comparing Clusterings
Qiaoliang Xiang, Qi Mao, Kian Ming Chai +3
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the…