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20222026
most citedSilent Guardian: Protecting Text from Malicious Exploitation by Large Language Models

12 citations · 101 across the 80 of their papers we have counts for

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Showing 2024Show all

19 papers · 1 filter

cs.CV2024★ 1 cited

FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention

Zhongyi Zhang, Jie Zhang, Wenbo Zhou +5

Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fr…

cs.CR2024★ 3 cited

AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?

Benlong Wu, Guoqiang Chen, Kejiang Chen +5

Penetration testing is essential to ensure Web security, which can detect and fix vulnerabilities in advance, and prevent data leakage and serious consequences. The powerful infere…

cs.CR2024

SQL Injection Jailbreak: A Structural Disaster of Large Language Models

Jiawei Zhao, Kejiang Chen, Weiming Zhang +1

Large Language Models (LLMs) are susceptible to jailbreak attacks that can induce them to generate harmful content. Previous jailbreak methods primarily exploited the internal prop…

cs.SE2024

Binary Code Similarity Detection via Graph Contrastive Learning on Intermediate Representations

Xiuwei Shang, Li Hu, Shaoyin Cheng +4

Binary Code Similarity Detection (BCSD) plays a crucial role in numerous fields, including vulnerability detection, malware analysis, and code reuse identification. As IoT devices…

cs.CV2024★ 1 cited

Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

Qidong Huang, Xiaoyi Dong, Pan Zhang +6

We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs…

cs.CR2024

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…

cs.CV2024★ 7 cited

Natias: Neuron Attribution based Transferable Image Adversarial Steganography

Zexin Fan, Kejiang Chen, Kai Zeng +3

Image steganography is a technique to conceal secret messages within digital images. Steganalysis, on the contrary, aims to detect the presence of secret messages within images. Re…

cs.CL2024

GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models

Kunsheng Tang, Wenbo Zhou, Jie Zhang +7

Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but they have also been observed to magnify societal biases, particularly those…

cs.CV2024★ 1 cited

UniForensics: Face Forgery Detection via General Facial Representation

Ziyuan Fang, Hanqing Zhao, Tianyi Wei +5

Previous deepfake detection methods mostly depend on low-level textural features vulnerable to perturbations and fall short of detecting unseen forgery methods. In contrast, high-l…

cs.CV2024

Rank-based No-reference Quality Assessment for Face Swapping

Xinghui Zhou, Wenbo Zhou, Tianyi Wei +5

Face swapping has become a prominent research area in computer vision and image processing due to rapid technological advancements. The metric of measuring the quality in most face…

cs.CR2024★ 1 cited

AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA

Weitao Feng, Wenbo Zhou, Jiyan He +6

Diffusion models have achieved remarkable success in generating high-quality images. Recently, the open-source models represented by Stable Diffusion (SD) are thriving and are acce…

cs.DB2024

SPSW: Database Watermarking Based on Fake Tuples and Sparse Priority Strategy

Zhiwen Ren, Zehua Ma, Weiming Zhang +1

Databases play a crucial role in storing and managing vast amounts of data in various organizations and industries. Yet the risk of database leakage poses a significant threat to d…

cs.CV2024★ 1 cited

Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models

Zijin Yang, Kai Zeng, Kejiang Chen +3

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution…

cs.AI2024

©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model

Chao Zhou, Huishuai Zhang, Jiang Bian +2

This paper addresses the contentious issue of copyright infringement in images generated by text-to-image models, sparking debates among AI developers, content creators, and legal…

cs.SE2024★ 1 cited

How Far Have We Gone in Binary Code Understanding Using Large Language Models

Xiuwei Shang, Shaoyin Cheng, Guoqiang Chen +6

Binary code analysis plays a pivotal role in various software security applications, such as software maintenance, malware detection, software vulnerability discovery, patch analys…

cs.CR2024

FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs

Xiuwei Shang, Guoqiang Chen, Shaoyin Cheng +4

Analyzing the behavior of cryptographic functions in stripped binaries is a challenging but essential task. Cryptographic algorithms exhibit greater logical complexity compared to…

cs.CR2024

Provably Secure Disambiguating Neural Linguistic Steganography

Yuang Qi, Kejiang Chen, Kai Zeng +2

Recent research in provably secure neural linguistic steganography has overlooked a crucial aspect: the sender must detokenize stegotexts to avoid raising suspicion from the eavesd…

cs.CL2024

Towards Generalist Prompting for Large Language Models by Mental Models

Haoxiang Guan, Jiyan He, Shuxin Zheng +3

Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed.…

cs.CR2024

Model X-ray:Detecting Backdoored Models via Decision Boundary

Yanghao Su, Jie Zhang, Ting Xu +3

Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs), enabling them to operate normally on clean inputs but manipulate predictions when specif…