Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks159 from public data
- Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models233
Gaussian Shading is proposed, a diffusion model watermarking technique that is both performance-lossless and training-free, while serving the dual purpose of copyright protection and tracing of offending content.
- 220
- LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks130
The proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously, and is the first generation based 3D point cloud attack method.
- Adversarial Examples Against Deep Neural Network based Steganalysis108
A new strategy is proposed that constructs enhanced covers against neural networks with the technique of adversarial examples and makes a tradeoff between the two analysis systems to improve the comprehensive security.
- Watermarking Text Generated by Black-Box Language Models103
A watermarking framework for black-box language model usage scenarios and results under re-translation, polishing, word deletion, and synonym substitution attacks reveal that it is arduous to remove the watermark without compromising the original semantics.
- Tracing Text Provenance via Context-Aware Lexical Substitution102
A natural language watermarking scheme based on context-aware lexical substitution (LS) that employs BERT to suggest LS candidates by inferring the semantic relatedness between the candidates and the original sentence and has a better transferability than existing methods.
- Flow-Based Robust Watermarking with Invertible Noise Layer for Black-Box Distortions95
Compared with the state-of-the-art architecture, the visual quality of the proposed framework improves by 2dB and the extraction accuracy after JPEG compression improves by more than 4%.
- 92
- Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective89
This work proposes a novel method that crafts adversarial examples by manipulating the distribution of the image and can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios.
- 77
- Robust adaptive steganography based on generalized dither modulation and expanded embedding domain71
The purpose of this paper is to refine the robust steganographic scheme by considering asymmetric costs for different modification polarities and expanding the embedding domain for digital images, aiming to aggregate the modifications on the elements with small costs.
- A Closer Look at Machine Unlearning for Large Language Models63
To address the issue of inadequate evaluation of model outputs after unlearning, three additional metrics are introduced to evaluate token diversity, sentence semantics, and factual correctness and to alleviate issues in machine unlearning for LLMs.
- StegaDDPM: Generative Image Steganography based on Denoising Diffusion Probabilistic Model60
StegaDDPM outperforms state-of-the-art methods in terms of steganographic capacity, extraction accuracy, and security, and can securely conceal and accurately extract secret messages up to 9 bits per pixel.
- 58
- 57
- 55
- 53
- Provably Secure Generative Steganography Based on Autoregressive Model43
This paper proposes a novel framework for generative steganography based on autoregressive model, or rather, PixelCNN, and shows that the proposed method can resist the existing steganalytic methods.
- 39
- Adversarial batch image steganography against CNN-based pooled steganalysis37
This paper proposes a general framework of adversarial embedding for batch steganography, in which a new loss function is designed and the batch strategy is combined with adversarialembedding, and shows the proposed method significantly improves the security performance of batch Steganography against pooled steganalysis and keeps a high-security level against single image Steganalysis.
- Defining Joint Distortion for JPEG Steganography37
This paper inspects the embedding change from the spatial domain and proposes a principle of Block Boundary Continuity (BBC) for defining JPEG joint distortion, which aims to restrain blocking artifacts caused by inter-block adjacent modifications and thus effectively preserve the spatial continuity at block boundaries.
- 36
- LDStega: Practical and Robust Generative Image Steganography based on Latent Diffusion Models35
Experimental results demonstrate that LDStega achieves high extraction accuracy while controllably generating image content and saving the stego image in the widely used PNG and JPEG formats and outperforms state-of-the-art techniques in resisting common image attacks.
- 33
- 32
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.