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An embedding approach using orthogonal matrices of the singular value decomposition for image steganography

机译:一种利用图像隐写奇异值分解的正交矩阵的嵌入方法

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This paper aims to reduce the embedding errors, maintain the image fidelity, and reduce the errors, when detecting the embedded messages in images. An embedding approach is proposed that depends on using the orthogonal matrices of the Singular Value Decomposition (SVD) as a vessel for embedding information instead of embedding in the singular values of the images. Three ways are suggested to reduce the embedding errors and maintain the image fidelity, when detecting the embedded message. These ways are increasing the number of columns protected without embedding, choosing the suitable block size to embed in and adjusting the singular values in order to give a high qualify of the stego image. Results show that utilization of the orthogonal matrices of the SVD for information hiding can be as effective as using transform-based techniques, and it gives better results than those obtained with the Least Significant Bit (LSB) technique.
机译:本文旨在减少嵌入错误,维护图像保真度,并在检测到图像中的嵌入消息时减少错误。提出了一种嵌入方法,其取决于使用奇异值分解(SVD)的正交矩阵作为用于嵌入信息而不是在图像的奇异值中嵌入的血管。建议在检测到嵌入消息时,建议减少嵌入错误并维护图像保真度。这些方式正在增加保护的列数而不嵌入,选择合适的块尺寸以嵌入并调整奇异值,以便提供SEGO图像的高鉴用性。结果表明,使用基于变换的技术的信息隐藏的SVD的正交矩阵的利用可以是有效的,并且它提供比用最低有效位(LSB)技术获得的结果更好的结果。

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