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DOI: 10.14569/IJACSA.2021.0120804
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Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion

Author 1: Kohei Arai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

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Abstract: Data hiding method with Principal Component Analysis (PCA) and image coordinate conversion as a preprocessing of wavelet Multi Resolution Analysis (MRA) is proposed. The method introduced in this paper, based on the characteristics of the original multispectral image, allows recovering the secret data. Through experiments, it is found that the proposed method is superior to the conventional data hiding method without any preprocessing. The method introduced in this paper allows only I who knows the characteristics of the original multispectral image to recover the secret data, i.e., when the information of the original image needs to be protected. Moreover, in the introduced method, the information of the secret data is protected by the existence of the eigenvector and the oblique coordinate transformation, that is, the secret data is restored if at least the information of the true original image is not known. The principal component transformation coefficient differs for each original image and is composed of the eigenvectors of the original image.

Keywords: Multi-dimensional wavelet transformation; multi resolution analysis (MRA); image data hiding; secrete image; Daubechies basis function

Kohei Arai, “Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120804

@article{Arai2021,
title = {Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120804},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120804},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Kohei Arai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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