基于分布保持嵌入与正交映射的鲁棒生成式图像隐写方法

袁程胜; 王昊宇; 尹青伟; 曹燚; 刘庆程; 付章杰

doi:10.11959/j.issn.1000-436x.2026063

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基于分布保持嵌入与正交映射的鲁棒生成式图像隐写方法

学术通信 | 更新时间：2026-05-07

- 基于分布保持嵌入与正交映射的鲁棒生成式图像隐写方法
- Robust generative image steganography method based on distribution-preserving embedding and orthogonal mapping
- 通信学报 2026年47卷第4期页码：288-298
- 作者机构：
  
  1.南京信息工程大学计算机学院、网络空间安全学院，江苏南京 210044
  2.南京信息工程大学数字取证教育部工程研究中心，江苏南京 210044
  3.江苏省教育厅教育宣传中心，江苏南京 210036
  4.无锡学院物联网工程学院，江苏无锡 214105
- 作者简介：
  
  [ "袁程胜（1989- ），男，江苏东海人，南京信息工程大学副教授，主要研究方向为人工智能安全、信息隐藏。" ]
  [ "王昊宇（2002- ），男，江苏扬州人，南京信息工程大学硕士生，主要研究方向为信息隐藏。" ]
  [ "尹青伟（1989- ），男，江苏淮安人，江苏省教育厅教育宣传中心中级工程师，主要研究方向为网络态势感知、网络空间治理等。" ]
  [ "曹燚（1994- ），男，江苏宿迁人，无锡学院副教授，主要研究方向为信息隐藏。" ]
  [ "刘庆程（1987- ），男，安徽滁州人，南京信息工程大学博士生，主要研究方向为多媒体内容安全、电力大数据挖掘与分析。" ]
  [ "付章杰（1983- ），男，河南南阳人，南京信息工程大学教授，主要研究方向为信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U22B2062;U23B2023;62372125)
- DOI：10.11959/j.issn.1000-436x.2026063
  中图分类号： TP391
- 收稿：2025-12-30，
  
  修回：2026-03-01，
  
  录用：2026-03-02，
  
  纸质出版：2026-04-20
- 稿件说明：
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袁程胜,王昊宇,尹青伟等.基于分布保持嵌入与正交映射的鲁棒生成式图像隐写方法[J].通信学报,2026,47(04):288-298.

Yuan Chengsheng,Wang Haoyu,Yin Qingwei,et al.Robust generative image steganography method based on distribution-preserving embedding and orthogonal mapping[J].Journal on Communications,2026,47(04):288-298.
袁程胜,王昊宇,尹青伟等.基于分布保持嵌入与正交映射的鲁棒生成式图像隐写方法[J].通信学报,2026,47(04):288-298. DOI： 10.11959/j.issn.1000-436x.2026063.

Yuan Chengsheng,Wang Haoyu,Yin Qingwei,et al.Robust generative image steganography method based on distribution-preserving embedding and orthogonal mapping[J].Journal on Communications,2026,47(04):288-298. DOI： 10.11959/j.issn.1000-436x.2026063.

摘要

在协同多智能体系统的隐蔽通信中，基于生成式模型的隐写技术能够直接在公开信道中合成载体图像，为安全传输指令或数据提供了新途径。针对现有生成式隐写方法无法兼顾鲁棒性、载体图像质量与抗检测性等问题，提出了一种基于分布保持嵌入与正交映射的生成式图像隐写方法。首先，设计一种分布保持的信息嵌入机制，将秘密信息编码至采样的高斯潜向量中。其次，引入向量重构模块，生成满足标准正态分布的向量作为模型输入，以增强对信道干扰的鲁棒性及抗隐写分析能力。最后，接收方基于共享的密钥执行逆向操作以提取信息。实验结果表明，在不同隐写容量下，所提方法生成的载体图像均具有较好的视觉保真度，在遭受JPEG压缩、高斯噪声等常见信道攻击后，信息提取准确率仍保持在97%以上，表现出较强的鲁棒性。此外，面对常见的隐写分析检测器，误检率均保持在0.500左右，表现出优异的抗隐写分析性能。

Abstract

In covert communication for collaborative multi-agent systems

generative model-based image steganography can directly synthesize stego-images over public channels

offering a novel approach for secure transmission of instructions and data. Given that existing generative steganography methods can’t simultaneously achieve robustness

cover image quality

and resistance to detection

a generative image steganography method based on distribution-preserving embedding and orthogonal mapping was proposed. Firstly

a distribution-preserving information embedding mechanism was designed to embed the secret information into the sampled Gaussian latent vector. Secondly

a vector reconstruction module was introduced to generate inputs that follow the standard normal distribution

thereby enhancing robustness against channel interference and resistance to steganalysis. Finally

the receiver performed inverse operations based on the shared key to extract the information. Experimental results show that

under different steganographic capacities

the cover images generated by the proposed method consistently achieve superior visual fidelity. After attacks such as JPEG compression and Gaussian noise

the information extraction accuracy remains above 97%

demonstrating strong robustness. Against common steganalysis detectors

the false positive rate is approximately 0.500

highlighting excellent detectability resistance.

关键词

Keywords

references

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