14 citations · 36 across the 36 of their papers we have counts for
12 papers · 1 filter
ASCENT: First-Order Optimal Fine-Tuning with Recalibration for Safety--Utility Co-Enhancement
Weiwei Qi, Chongyu Wang, Tianhang Zheng +5
Supervised fine-tuning can substantially improve the downstream utility of large language models (LLMs) but may compromise their safety. Existing safety-preserving methods constrai…
"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking
Xinyu Zhang, Ziping Dong, Qingyu Liu +3
The rapid advancement of generative AI has underscored the critical need for identifying image ownership and protecting copyrights. This makes post-processing image watermarking an…
Attack-Resistant Watermarking for AIGC Image Forensics via Diffusion-based Semantic Deflection
Qingyu Liu, Yitao Zhang, Zhongjie Ba +4
Protecting the copyright of user-generated AI images is an emerging challenge as AIGC becomes pervasive in creative workflows. Existing watermarking methods (1) remain vulnerable t…
FBAD: Frequency-based Black-box Attack for AI-generated Image Detection
Xiaojing Chen, Dan Li, Lijun Peng +6
The prosperous development of Artificial Intelligence-Generated Content (AIGC) has brought people's anxiety about the spread of false information on social media. Designing detecto…
WMCopier: Forging Invisible Image Watermarks on Arbitrary Images
Ziping Dong, Chao Shuai, Zhongjie Ba +4
Invisible Image Watermarking is crucial for ensuring content provenance and accountability in generative AI. While Gen-AI providers are increasingly integrating invisible watermark…
Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models
Yu He, Boheng Li, Liu Liu +6
Membership Inference Attacks (MIAs) aim to predict whether a data sample belongs to the model's training set or not. Although prior research has extensively explored MIAs in Large…
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features
Peng Cheng, Yuwei Wang, Peng Huang +5
Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial att…
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…
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning
Binhao Ma, Tianhang Zheng, Hongsheng Hu +5
Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with…
Phoneme-Based Proactive Anti-Eavesdropping with Controlled Recording Privilege
Peng Huang, Yao Wei, Peng Cheng +5
The widespread smart devices raise people's concerns of being eavesdropped on. To enhance voice privacy, recent studies exploit the nonlinearity in microphone to jam audio recorder…
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
Xinyu Zhang, Qingyu Liu, Zhongjie Ba +5
Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model. However, recent studies show that…
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
Xinyu Zhang, Hanbin Hong, Yuan Hong +4
The language models, especially the basic text classification models, have been shown to be susceptible to textual adversarial attacks such as synonym substitution and word inserti…