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Universal Steganalysis Method Based on Multi-domain Features

机译:基于多域特征的通用隐写分析方法

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摘要

A new method of universal steganalysis for BMP images, which had low embedding rate and based on multi-domain features, is proposed in this paper. It provided a way to extract statistical features from multi-domain for universal steganalysis and solved the problem of low detection rate for a small amount of data embedding. Features were extracted from gradient energy differences in spatial domain, correlation coefficient in DCT domain, and the mean and standard deviation of difference value matrix in DWT domain. Experiments results show that detection achieved a better reliability when the embedding capacity is above 2 KB, compared with existing methods.
机译:提出了一种基于多域特征的低嵌入率的BMP图像通用隐写分析新方法。它提供了一种从多域中提取统计特征进行通用隐写分析的方法,并解决了少量数据嵌入时检测率低的问题。从空间域中的梯度能量差,DCT域中的相关系数以及DWT域中差值矩阵的均值和标准差中提取特征。实验结果表明,与现有方法相比,当嵌入量大于2 KB时,检测可以获得更好的可靠性。

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