8 citations · 11 across the 25 of their papers we have counts for
8 papers · 1 filter
VideoEraser: Concept Erasure in Text-to-Video Diffusion Models
Naen Xu, Jinghuai Zhang, Changjiang Li +5
The rapid growth of text-to-video (T2V) diffusion models has raised concerns about privacy, copyright, and safety due to their potential misuse in generating harmful or misleading…
IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents
Hengyu An, Jinghuai Zhang, Tianyu Du +4
Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, whe…
Enkidu: Universal Frequential Perturbation for Real-Time Audio Privacy Protection against Voice Deepfakes
Zhou Feng, Jiahao Chen, Chunyi Zhou +4
The rapid advancement of voice deepfake technologies has raised serious concerns about user audio privacy, as attackers increasingly exploit publicly available voice data to genera…
LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing
Jiahao Chen, Junhao Li, Yiming Wang +6
The proliferation of Low-Rank Adaptation (LoRA) models has democratized personalized text-to-image generation, enabling users to share lightweight models (e.g., personal portraits)…
VModA: An Effective Framework for Adaptive NSFW Image Moderation
Han Bao, Qinying Wang, Zhi Chen +6
Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learnin…
Poison in the Well: Feature Embedding Disruption in Backdoor Attacks
Zhou Feng, Jiahao Chen, Chunyi Zhou +3
Backdoor attacks embed malicious triggers into training data, enabling attackers to manipulate neural network behavior during inference while maintaining high accuracy on benign in…
UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep Reinforcement Learning
Oubo Ma, Linkang Du, Yang Dai +4
Deep reinforcement learning (DRL) is widely applied to safety-critical decision-making scenarios. However, DRL is vulnerable to backdoor attacks, especially action-level backdoors,…
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
Jiale Zhang, Bosen Rao, Chengcheng Zhu +6
Graph Neural Networks (GNNs) have achieved remarkable performance through their message-passing mechanism. However, recent studies have highlighted the vulnerability of GNNs to bac…