基于显著性检测的图像隐写分析方法

doi:10.11772/j.issn.1001-9081.2020081323

计算机应用 ›› 2021, Vol. 41 ›› Issue (2): 441-448.DOI: 10.11772/j.issn.1001-9081.2020081323

所属专题：网络空间安全

基于显著性检测的图像隐写分析方法

黄思远^1,2, 张敏情^1,2, 柯彦^1,2, 毕新亮^1,2

1. 武警工程大学密码工程学院, 西安 710086;
2. 网络与信息安全武警部队重点实验室(武警工程大学), 西安 710086

收稿日期:2020-08-31 修回日期:2020-12-14 发布日期:2020-12-17 出版日期:2021-02-10
通讯作者: 张敏情
作者简介:黄思远(1997-),男,陕西西安人,硕士研究生,主要研究方向:信息隐藏;张敏情(1967-),女,陕西西安人,教授,博士,主要研究方向:信息安全、密码学、信息隐藏;柯彦(1991-),男,河南南阳人,博士,主要研究方向:信息隐藏、密码学;毕新亮(1997-),男,安徽合肥人,硕士研究生,主要研究方向:信息隐藏。
基金资助:
国家自然科学基金项资助项目（61872384）。

Image steganalysis method based on saliency detection

HUANG Siyuan^1,2, ZHANG Minqing^1,2, KE Yan^1,2, BI Xinliang^1,2

1. College of Cryptographic Engineering, Engineering University of PAP, Xi'an Shaanxi 710086, China;
2. Key Laboratory for Network and Information Security of PAP(Engineering University of PAP), Xi'an Shaanxi 710086, China

Received:2020-08-31 Revised:2020-12-14 Online:2020-12-17 Published:2021-02-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61872384).

摘要/Abstract

摘要： 针对图像隐写分析难度大、现有的检测模型难以对图像隐写区域进行针对性检测的问题，提出了一种基于显著性检测的图像隐写分析方法。该方法利用显著性检测技术引导隐写分析模型更加关注图像隐写区域的特征。首先，显著性检测模块生成图像的显著性区域；其次，区域筛选模块筛选出与隐写区域重合度较高的显著性图，利用图像融合技术与原始图像进行融合；最后，用相应的显著性融合图替换检测错误的图像，提高训练集质量，从而提升模型的训练效果和检测能力。实验使用BOSSbase1.01数据集，分别用空域和JPEG域的自适应隐写算法对数据集嵌入后进行隐写分析，结果表明，该方法能够有效提升现有基于深度学习的隐写分析模型的检测准确率，最多可提升3个百分点。为了进一步验证该方法的泛化性和提高其实用价值，在IStego100K数据集上进行了模型失配测试，测试结果也表明该方法具有一定的泛化能力。

关键词: 图像自适应隐写术, 图像隐写分析, 显著性检测, 图像融合, 深度学习

Abstract: Aiming at the problem that the steganalysis of images is difficult, and the existing detection models are difficult to make a targeted analysis of steganography regions of images, a method for image steganalysis based on saliency detection was proposed. In the proposed method, the saliency detection was used to guide the steganalysis model to focus on the image features of steganography regions. Firstly, the saliency detection module was used to generate saliency regions of the image. Secondly, the region filter module was used to filter the saliency images with a high degree of coincidence with the steganography regions, and image fusion technology was used to fuse them with the original images. Finally, the quality of training set was improved by replacing the error detection images with their corresponding saliency fusion images, so as to improve the training effect and detection ability of the model. The experiments were carried out on BOSSbase1.01 dataset. The dataset was embedded by image adaptive steganography algorithms in spatial domain and JPEG domain respectively, and experimental results show that the proposed method can effectively improve the the detection accuracy for deep learning-based steganalysis model by 3 percentage points at most. The mismatch test was also carried out on IStego100K dataset to further verify the generalization ability of the model and improve its application value. According to the result of the mismatch test, the proposed method has certain generalization ability.

Key words: adaptive image steganography, image steganalysis, saliency detection, image fusion, deep learning

中图分类号:

TP309.2

黄思远, 张敏情, 柯彦, 毕新亮. 基于显著性检测的图像隐写分析方法[J]. 计算机应用, 2021, 41(2): 441-448.

HUANG Siyuan, ZHANG Minqing, KE Yan, BI Xinliang. Image steganalysis method based on saliency detection[J]. Journal of Computer Applications, 2021, 41(2): 441-448.

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基于显著性检测的图像隐写分析方法

Image steganalysis method based on saliency detection

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