17 citations · 18 across the 16 of their papers we have counts for
16 papers
REED: Post-Training Representation Editing for Cross-Domain Linguistic Steganalysis
Ruohan Lei, Jianxin Gao, Wanli Peng +1
In real-world scenarios of linguistic steganalysis, tested texts usually come from unseen domains with different vocabularies, topics, writing styles, and steganographic generation…
Text Steganography with Dynamic Codebook and Multimodal Large Language Model
Jianxin Gao, Ruohan Lei, Wanli Peng
With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance. However, existing methods still have some issues: (1) For the white…
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…
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…
Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Wanli Peng, Xin Chen, Hang Fu +3
Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference abil…
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…
Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking
Ziwei Zhang, Juan Wen, Wanli Peng +3
Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized i…
ImF: Implicit Fingerprint for Large Language Models
Jiaxuan Wu, Wanli Peng, Hang Fu +2
Training large language models (LLMs) is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs…
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…
From Covert Hiding to Visual Editing: Robust Generative Video Steganography
Xueying Mao, Xiaoxiao Hu, Wanli Peng +5
Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic…
Improved CNN Prediction Based Reversible Data Hiding
Yingqiang Qiu, Wanli Peng, Xiaodan Lin +2
This letter proposes an improved CNN predictor (ICNNP) for reversible data hiding (RDH) in images, which consists of a feature extraction module, a pixel prediction module, and a c…