12 citations · 24 across the 20 of their papers we have counts for
21 papers
LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models
Yaopeng Wang, Qingliang Wang, Zhibo Wang +5
Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing text-to-image diffusion models, enabling lightweight modules that are shared, reused, and commercializ…
LoopTrap: Termination Poisoning Attacks on LLM Agents
Huiyu Xu, Zhibo Wang, Wenhui Zhang +4
Modern LLM agents solve complex tasks by operating in iterative execution loops, where they repeatedly reason, act, and self-evaluate progress to determine when a task is complete.…
RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry
Bo Lv, Zhiheng Xu, KeDong Xiu +4
As Mixture-of-Experts (MoE) architectures are increasingly adopted for scaling Large Language Models (LLMs), safety auditing becomes necessary to verify whether these models produc…
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
Bo Wang, Jia Ni, Mengnan Zhao +2
The unauthorized use of personal data in model training has emerged as a growing privacy threat. Unlearnable examples (UEs) address this issue by embedding imperceptible perturbati…
QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference
Jianxin Yan, Zeheng Qian, Wangze Ni +6
Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computational costs and improving effic…
RerouteGuard: Understanding and Mitigating Adversarial Risks for LLM Routing
Wenhui Zhang, Huiyu Xu, Zhibo Wang +4
Recent advancements in multi-model AI systems have leveraged LLM routers to reduce computational cost while maintaining response quality by assigning queries to the most appropriat…
RBD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images
Qingyu Liu, Zhongjie Ba, Jianmin Guo +4
Recently, reconstruction-based methods have gained attention for AIGC image detection. These methods leverage pre-trained diffusion models to reconstruct inputs and measure residua…
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
Xuran Li, Jingyi Wang, Xiaohan Yuan +1
It is often desirable to remove (a.k.a. unlearn) a specific part of the training data from a trained neural network model. A typical application scenario is to protect the data hol…
PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning
Yaopeng Wang, Huiyu Xu, Zhibo Wang +5
Watermarking for diffusion images has drawn considerable attention due to the widespread use of text-to-image diffusion models and the increasing need for their copyright protectio…
Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaboration
Shuaifan Jin, Xiaoyi Pang, Zhibo Wang +4
Recent studies improve on-device language model (LM) inference through end-cloud collaboration, where the end device retrieves useful information from cloud databases to enhance lo…
Can Small Language Models Reliably Resist Jailbreak Attacks? A Comprehensive Evaluation
Zhibo Wang, Wenhui Zhang, Huiyu Xu +3
Small language models (SLMs) have emerged as promising alternatives to large language models (LLMs) due to their low computational demands, enhanced privacy guarantees, and compara…
RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent
Huiyu Xu, Wenhui Zhang, Zhibo Wang +5
Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expand…
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Zhibo Wang, Zhiwei Chang, Jiahui Hu +4
Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privac…
Textual Unlearning Gives a False Sense of Unlearning
Jiacheng Du, Zhibo Wang, Jie Zhang +3
Language Models (LMs) are prone to ''memorizing'' training data, including substantial sensitive user information. To mitigate privacy risks and safeguard the right to be forgotten…
SoK: On Gradient Leakage in Federated Learning
Jiacheng Du, Jiahui Hu, Zhibo Wang +4
Federated learning (FL) facilitates collaborative model training among multiple clients without raw data exposure. However, recent studies have shown that clients' private training…
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Chao Shuai, Jieming Zhong, Shuang Wu +6
Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to ima…
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
Kun Pan, Yin Yifang, Yao Wei +6
The malicious use and widespread dissemination of deepfake pose a significant crisis of trust. Current deepfake detection models can generally recognize forgery images by training…
SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution
Zhongjie Ba, Jieming Zhong, Jiachen Lei +5
Advanced text-to-image models such as DALLE 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential p…
Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization
Yuheng Yang, Haipeng Chen, Zhenguang Liu +5
Action recognition has long been a fundamental and intriguing problem in artificial intelligence. The task is challenging due to the high dimensionality nature of an action, as wel…
Privacy-preserving Adversarial Facial Features
Zhibo Wang, He Wang, Shuaifan Jin +7
Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for…
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Zhibo Wang, Xiaowei Dong, Henry Xue +4
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants…