6 papers
Less Is More -- Until It Breaks: Security Pitfalls of Vision Token Compression in Large Vision-Language Models
Xiaomei Zhang, Zhaoxi Zhang, Leo Yu Zhang +3
Visual token compression is widely adopted to improve the inference efficiency of Large Vision-Language Models (LVLMs), enabling their deployment in latency-sensitive and resource-…
TED++: Submanifold-Aware Backdoor Detection via Layerwise Tubular-Neighbourhood Screening
Nam Le, Leo Yu Zhang, Kewen Liao +2
As deep neural networks power increasingly critical applications, stealthy backdoor attacks, where poisoned training inputs trigger malicious model behaviour while appearing benign…
Character-Level Perturbations Disrupt LLM Watermarks
Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang +5
Large Language Model (LLM) watermarking embeds detectable signals into generated text for copyright protection, misuse prevention, and content detection. While prior studies evalua…
BiMark: Unbiased Multilayer Watermarking for Large Language Models
Xiaoyan Feng, He Zhang, Yanjun Zhang +2
Recent advances in Large Language Models (LLMs) have raised urgent concerns about LLM-generated text authenticity, prompting regulatory demands for reliable identification mechanis…
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
Zirui Gong, Yanjun Zhang, Leo Yu Zhang +3
Federated Ranking Learning (FRL) is a state-of-the-art FL framework that stands out for its communication efficiency and resilience to poisoning attacks. It diverges from the tradi…
Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks
Xiaomei Zhang, Zhaoxi Zhang, Yanjun Zhang +4
Textual adversarial examples pose serious threats to the reliability of natural language processing systems. Recent studies suggest that adversarial examples tend to deviate from t…