7 papers
Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models
Hang Fu, Wanli Peng, Yinghan Zhou +3
The widespread adoption of Large Language Model (LLM) in commercial and research settings has intensified the need for robust intellectual property protection. Backdoor-based LLM f…
Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
Yinghan Zhou, Juan Wen, Wanli Peng +3
AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliabi…
EditMF: Drawing an Invisible Fingerprint for Your Large Language Models
Jiaxuan Wu, Yinghan Zhou, Wanli Peng +3
Training large language models (LLMs) is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs…
From Implicit to Explicit: Enhancing Self-Recognition in Large Language Models
Yinghan Zhou, Weifeng Zhu, Juan Wen +3
Large language models (LLMs) have been shown to possess a degree of self-recognition ability, which used to identify whether a given text was generated by themselves. Prior work ha…
BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation
Zhengxian Wu, Juan Wen, Wanli Peng +3
Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of…
GTSD: Generative Text Steganography Based on Diffusion Model
Zhengxian Wu, Juan Wen, Yiming Xue +2
With the rapid development of deep learning, existing generative text steganography methods based on autoregressive models have achieved success. However, these autoregressive steg…