14 citations · 16 across the 5 of their papers we have counts for
6 papers
Towards Merging-conditional Backdoor Unalignment against Large Language Models
Yukun Chen, Yidou Chen, Yiming Li +4
Model merging has become widely adopted for enhancing the task-specific performance of Large Language Models (LLMs) without additional training. While the specialized model from th…
"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…
Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation
Zhongjie Ba, Yitao Zhang, Peng Cheng +4
Watermarking plays a key role in the provenance and detection of AI-generated content. While existing methods prioritize robustness against real-world distortions (e.g., JPEG compr…
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…
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
Hanbin Hong, Xinyu Zhang, Binghui Wang +2
Black-box adversarial attacks have demonstrated strong potential to compromise machine learning models by iteratively querying the target model or leveraging transferability from a…