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International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 5 Issue 12, 2016.
Abstract: Data hiding method based on steganography improving secret image invisibility through replacing Least Significant Bit: LSB with run-length coded Image is proposed. Although, the proposed method is based on Discrete Wavelet Transformation: DWT, run-length coded secrete image is embedded in the LSB of the high frequency component before reconstruction of the image for opening to the public for improvement of the secrete image invisibility. Before embedding the coded secret image, the coded secret image bits are rearranged the order by using random number. Therefore, secret image invisibility is much improved. Through experiments, it is confirmed that secret images are almost invisible in distribute images. Data hiding performance in terms of invisibility of secret images which are embedded in distribute images is evaluated with the Peak Signal to Noise Ratio: PSNR and the Root Mean Square: RMS difference between the original secret image and extracted one from the distribute images. Meanwhile the conventional Multi-Resolution Analysis: MRA based data hiding is attempted with a variety of parameters, level of MRA and the frequency component location of which secret image is replaced to it and is compared to the proposed method. It is found that the proposed data hiding method is superior to the conventional method.
Kohei Arai, “Data Hiding Method Replacing LSB of Hidden Portion for Secret Image with Run-Length Coded Image” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(12), 2016. http://dx.doi.org/10.14569/IJARAI.2016.051202
@article{Arai2016,
title = {Data Hiding Method Replacing LSB of Hidden Portion for Secret Image with Run-Length Coded Image},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2016.051202},
url = {http://dx.doi.org/10.14569/IJARAI.2016.051202},
year = {2016},
publisher = {The Science and Information Organization},
volume = {5},
number = {12},
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.