25 citations · 31 across the 10 of their papers we have counts for
6 papers · 1 filter
Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks
Xiaoyan Feng, Yanjun Zhang, He Zhang +2
Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this same robustness allows an adver…
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-…
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…
Large Language Model Watermark Stealing With Mixed Integer Programming
Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang +5
The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preven…
Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation
Di Mi, Yanjun Zhang, Leo Yu Zhang +4
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only querying its API service remotely, posing a se…
Client-side Gradient Inversion Against Federated Learning from Poisoning
Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang +5
Federated Learning (FL) enables distributed participants (e.g., mobile devices) to train a global model without sharing data directly to a central server. Recent studies have revea…