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应用科学学报 >

2024 , Vol. 42 >Issue 3: 469 - 485

DOI: https://doi.org/10.3969/j.issn.0255-8297.2024.03.009

数字媒体取证与安全专栏

面向编码伪装的鲁棒无载体图像隐写方法

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  • 1. 江西财经大学 信息管理学院, 江西 南昌 330013;
    2. 南京航空航天大学 计算机科学与技术学院, 江苏 南京 211106

收稿日期: 2023-11-27

  网络出版日期: 2024-06-06

基金资助

国家自然科学基金（No. 62201233, No. 61961022, No. 61906079）资助

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A Robust Coverless Image Steganography Method for Coding Camouflage

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  • 1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China;
    2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Received date: 2023-11-27

  Online published: 2024-06-06

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摘要

传统的图像隐写方法容易受到隐写分析的攻击，无载体图像隐写方法可以从本质上抵抗隐写分析的攻击。然而，现有的无载体图像隐写算法存在鲁棒性低、提取精度有限和不可感知性差等问题，为此提出一种面向编码伪装的鲁棒无载体图像隐写方法。该方案将基于深度的合成隐写方法与传统的聚类相结合，通过感知哈希对编码网络生成的合成图像与相似图像进行匹配，将传输的图像由合成图像替换成真实自然图像以此提高安全性；进一步，利用聚类算法找到与相似图像对应的伪装图像进行传输。以卷积神经网络特征为基准进行聚类，提高了抗几何攻击的能力。实验分析表明，所提方案在隐藏容量、提取精度方面都有较优的表现，且解决了生成式隐写方案存在的图像质量低和鲁棒性差等问题。

关键词： 无载体图像隐写; 卷积神经网络聚类; 感知哈希; 生成网络; 伪装图像

本文引用格式

苑紫烨, 邱宝林, 叶妤, 温文, 化定丽, 张玉书 . 面向编码伪装的鲁棒无载体图像隐写方法[J]. 应用科学学报, 2024 , 42(3) : 469 -485 . DOI: 10.3969/j.issn.0255-8297.2024.03.009

Abstract

Traditional image steganography methods are susceptible to attack by steganalysis tools, whereas coverless image steganography method can essentially resist the attack of steganalyzers. However, most coverless image steganography algorithms suffer from problems such as low robustness, limited extraction accuracy, and poor imperceptibility. Therefore, this paper proposes a robust coverless steganography method for coding camouflage, which combines depth-based synthetic steganography with traditional clustering algorithms. The proposed algorithm matches the synthetic images generated by the coding network with similar images through perceptual hashing, and converts the transmitted images from synthetic images to real natural images to improve security. In addition, clustering algorithm is used to find the camouflage image which is corresponding to the similar image for transmission. The clustering is based on the convolutional neural networks (CNN) feature, which improves the ability to resist geometric attacks. Experimental analysis demonstrates that the proposed scheme achieves higher capacity and extraction accuracy, and solves the problems of low image quality and poor robustness of generative steganography schemes.

Key words： coverless image steganography; convolutional neural networks (CNN) clustering; perceptual hash; generate networks; camouflage image

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