Universal Steganalysis for image Based on Genetic Algorithm and Grey-SVC

Yuehong Wu, Zhuang Shen, Lihong Ma, Zixian Feng
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Abstract

The isolated samples can produce some effect on distinguishing the best classifying plane, which becomes one of causes of less performance of universal steganalysis that uses Support Vector Machines (SVM) as classifier. This paper proposes a new universal steganalysis algorithm for image based on Genetic Algorithm (GA) and Grey Support Vector Machines (GSVM). The algorithm firstly catches characteristic of noise signal in wavelet domain of image, then utilizes GA search samples which are used to train, and finds the best characteristic of species, finally makes grey relational degree between sample characteristic and the best characteristic of species participate in training of SVM, thus constructs a GSVM to be a classifier of steganalysis. The result testing on the large numbers of images indicates that the proposed universal steganalysis algorithm has less false positive rate and better classifying performance compared to Holotyak’s algorithm which has the same characteristic with above algorithm, which indicates that GSVM can reduce effect of isolated samples.
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基于遗传算法和 Grey-SVC 的通用图像隐写分析技术
孤立样本会对区分最佳分类平面产生一定影响,这也是使用支持向量机(SVM)作为分类器的通用隐写分析性能较差的原因之一。本文提出了一种基于遗传算法(GA)和灰色支持向量机(GSVM)的新型通用图像隐写分析算法。该算法首先在图像的小波域中捕捉噪声信号的特征,然后利用遗传算法搜索用于训练的样本,找出最佳的物种特征,最后使样本特征与最佳物种特征之间的灰色关联度参与 SVM 的训练,从而构建一个 GSVM 作为隐写分析的分类器。对大量图像的测试结果表明,与具有相同特征的 Holotyak 算法相比,所提出的通用隐写分析算法的误判率更低,分类性能更好,这表明 GSVM 可以减少孤立样本的影响。
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