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基于混沌系统的三种隐写方法及其基于三种特征向量的通用隐写分析的比较研究

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

作者信息

Battikh Dalia, El Assad Safwan, Hoang Thang Manh, Bakhache Bassem, Deforges Olivier, Khalil Mohamad

机构信息

LASTRE Laboratory, Lebanese University, 210 Tripoli, Lebanon.

Institut d'Electronique et des Télécommunications de Rennes (IETR), UMR CNRS 6164, Université de Nantes-Polytech Nantes, Rue Christian Pauc CS 50609, CEDEX 3, 44306 Nantes, France.

出版信息

Entropy (Basel). 2019 Jul 30;21(8):748. doi: 10.3390/e21080748.

DOI:10.3390/e21080748
PMID:33267462
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7515277/
Abstract

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

摘要

在本文中，我们首先利用一种提出的混沌系统研究了三种隐写方法的安全性增强。第一种方法，即基于重新审视的最低有效位匹配的增强边缘自适应图像隐写术（EEALSBMR），存在于空间域。另外两种方法，增强离散余弦变换（EDCT）和增强离散小波变换（EDWT），存在于频域。该混沌系统极其健壮，由一个强大的混沌发生器和一个二维猫映射组成。其主要作用是在检测到消息时保护消息内容的安全。其次，使用基于多分辨率小波分解的三种盲隐写分析方法来检测嵌入的消息是否隐藏在测试图像（隐写图像）中或未隐藏在测试图像（载体图像）中。隐写分析方法基于这样的假设，即消息嵌入方案会在图像中留下可用于检测的统计证据或结构。仿真结果表明，如果在EEALSBMR隐写方法中消息大小小于20%，以及在EDCT隐写方法中消息大小小于15%，支持向量机（SVM）分类器和Fisher线性判别（FLD）无法区分载体图像和隐写图像。然而，在EDWT隐写方法中，无论消息大小如何，SVM和FLD都能以合理的准确率区分载体图像和隐写图像。

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3
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4
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IEEE Trans Neural Netw. 1999;10(5):988-99. doi: 10.1109/72.788640.