7 citations · 15 across the 18 of their papers we have counts for
11 papers · 1 filter
PURA: Provably Unbiased and Robust Multi-Bit Watermarking for AI-Generated Text Attribution
Yaofei Wang, Jinyang Guo, Shuchao Du +6
Fine-grained attribution of AI-generated text is becoming increasingly important for accountability and auditing, yet existing multi-bit watermarking methods still struggle to simu…
Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models
Xingkai Peng, Jun Jiang, Jiayang Liu +2
Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapte…
Into the Gray Zone: Domain Contexts Can Blur LLM Safety Boundaries
Ki Sen Hung, Xi Yang, Chang Liu +7
A central goal of LLM alignment is to balance helpfulness with harmlessness, yet these objectives conflict when the same knowledge serves both legitimate and malicious purposes. Th…
EditMark: Watermarking Large Language Models based on Model Editing
Shuai Li, Kejiang Chen, Jun Jiang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources, rendering them valuable digital asse…
Membership Inference Attacks on Tokenizers of Large Language Models
Meng Tong, Yuntao Du, Kejiang Chen +2
Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks are applied to pre-trained larg…
Provably Secure Public-Key Steganography Based on Admissible Encoding
Xin Zhang, Kejiang Chen, Na Zhao +2
The technique of hiding secret messages within seemingly harmless covertext to evade examination by censors with rigorous security proofs is known as provably secure steganography…
GIFDL: Generated Image Fluctuation Distortion Learning for Enhancing Steganographic Security
Xiangkun Wang, Kejiang Chen, Yuang Qi +3
Minimum distortion steganography is currently the mainstream method for modification-based steganography. A key issue in this method is how to define steganographic distortion. Wit…
SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling
Yaofei Wang, Gang Pei, Kejiang Chen +5
Steganography embeds confidential data within seemingly innocuous communications. Provable security in steganography, a long-sought goal, has become feasible with deep generative m…
On the Vulnerability of Text Sanitization
Meng Tong, Kejiang Chen, Xiaojian Yuan +4
Text sanitization, which employs differential privacy to replace sensitive tokens with new ones, represents a significant technique for privacy protection. Typically, its performan…
Prefix Guidance: A Steering Wheel for Large Language Models to Defend Against Jailbreak Attacks
Jiawei Zhao, Kejiang Chen, Xiaojian Yuan +1
In recent years, the rapid development of large language models (LLMs) has achieved remarkable performance across various tasks. However, research indicates that LLMs are vulnerabl…
InferDPT: Privacy-Preserving Inference for Closed-box Large Language Model
Meng Tong, Kejiang Chen, Jie Zhang +5
Large language models (LLMs), like ChatGPT, have greatly simplified text generation tasks. However, they have also raised concerns about privacy risks such as data leakage and unau…