8 citations · 25 across the 24 of their papers we have counts for
25 papers
Fingerprinting Multimodal Large Language Models
Chao Huang, Meng Tong, Kejiang Chen
While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable to illicit deployment and unaut…
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…
FARI: Robust One-Step Inversion for Watermarking in Diffusion Models
Jindong Yang, Han Fang, Weiming Zhang +2
Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that is both slow and error-prone. W…
DNA: Dual-stage Native Attribution for Generated Image Source Tracing
Chao Wang, Kejiang Chen, Zijin Yang +4
The rapid evolution of image generation has produced numerous within-family variants, making source-model attribution of suspect images increasingly important for digital forensics…
PTL-Diffusion: Manifold-Aware Diffusion with Periodic Terminal Laws
Danqi Zhuang, Jisui Huang, Xiaoyue Xi +4
Standard diffusion models typically use a single time-homogeneous Gaussian terminal distribution as the reference law for generation. While this choice is analytically convenient a…
BareWave: Waveform-Native Flow-Matching Text-to-Speech
Wei Fan, Chao-Hong Tan, Qian Chen +5
Removing intermediate representations and separately trained decoding stages has become an important direction in generative modeling. In text-to-speech, however, high-quality syst…
ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography
Yaofei Wang, Rui Wang, Weilong Pang +4
Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however,…
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…
LiteUpdate: A Lightweight Framework for Updating AI-Generated Image Detectors
Jiajie Lu, Zhenkan Fu, Na Zhao +4
The rapid progress of generative AI has led to the emergence of new generative models, while existing detection methods struggle to keep pace, resulting in significant degradation…
A high-capacity linguistic steganography based on entropy-driven rank-token mapping
Jun Jiang, Weiming Zhang, Nenghai Yu +1
Linguistic steganography enables covert communication through embedding secret messages into innocuous texts; however, current methods face critical limitations in payload capacity…
T2SMark: Balancing Robustness and Diversity in Noise-as-Watermark for Diffusion Models
Jindong Yang, Han Fang, Weiming Zhang +2
Diffusion models have advanced rapidly in recent years, producing high-fidelity images while raising concerns about intellectual property protection and the misuse of generative AI…
AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction
Chao Wang, Zijin Yang, Yaofei Wang +2
The rapid advancement of image-generation technologies has made it possible for anyone to create photorealistic images using generative models, raising significant security concern…
Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models
Zijin Yang, Xin Zhang, Kejiang Chen +5
Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution…
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…
Provably Secure Robust Image Steganography via Cross-Modal Error Correction
Yuang Qi, Kejiang Chen, Na Zhao +2
The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably sec…
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…
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…
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…
Invertible Image Dataset Protection
Kejiang Chen, Xianhan Zeng, Qichao Ying +3
Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated model…
Tracing Text Provenance via Context-Aware Lexical Substitution
Xi Yang, Jie Zhang, Kejiang Chen +4
Text content created by humans or language models is often stolen or misused by adversaries. Tracing text provenance can help claim the ownership of text content or identify the ma…
Improving Dither Modulation based Robust Steganography by Overflow Suppression
Kai Zeng, Kejiang Chen, Yaofei Wang +2
Nowadays, people are sharing their pictures on online social networks (OSNs), so OSN is a good platform for Steganography. But OSNs usually perform JPEG compression on the uploaded…
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition
Haozhe Chen, Weiming Zhang, Kunlin Liu +3
As an effective method for intellectual property (IP) protection, model watermarking technology has been applied on a wide variety of deep neural networks (DNN), including speech c…