6 papers
Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures
Zeyao Liu, Zhendong Zhao, Xiaojun Chen +3
Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forced adversaries towards the V…
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…
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…
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…