17 citations · 18 across the 8 of their papers we have counts for
8 papers
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…
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…
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
Zhengxian Wu, Juan Wen, Wanli Peng +3
Customized Large Language Model (LLM) agents face a critical security threat from black-box instruction backdoors, where malicious behaviors are covertly injected through hidden sy…
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…
BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models
Zhengxian Wu, Juan Wen, Wanli Peng +3
Previous insertion-based and paraphrase-based backdoors have achieved great success in attack efficacy, but they ignore the text quality and semantic consistency between poisoned a…
Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations
Yinghan Zhou, Juan Wen, Wanli Peng +3
The growing popularity of large language models has raised concerns regarding the potential to misuse AI-generated text (AIGT). It becomes increasingly critical to establish an exc…
Generative Text Steganography with Large Language Model
Jiaxuan Wu, Zhengxian Wu, Yiming Xue +2
Recent advances in large language models (LLMs) have blurred the boundary of high-quality text generation between humans and machines, which is favorable for generative text stegan…