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DOI: 10.14569/IJACSA.2020.0110959
PDF

Computational Analysis of Arabic Cursive Steganography using Complex Edge Detection Techniques

Author 1: Anwar H. Ibrahim
Author 2: Abdulrahman S. Alturki

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.

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Abstract: Arabic language contains a multiple set of features which complete the process of embedding and extracting text from the compressed image. In specific, the Arabic language covers numerous textual styles and shapes of the Arabic letter. This paper investigated Arabic cursive steganography using complex edge detection techniques via compressed image, which comprises several characteristics (short, medium and Long sentence) as per the research interest. Sample of images from the Berkeley Segmentation Database (BSD) was utilized and compressed with a diverse number of bits per pixel through Least Significant Bit (LSB) technology. The method presented in this paper was evaluated based on five complex edge detectors (Roberts, Prewitt, Sobel, LoG, and Canny) via MATLAB. Canny edge detector has been demonstrated to be the most excellent solution when it is vital to perform superior edge discovery over-compressed image with little several facts, but Sobel appears to be better in term of the execution time for Long sentence contents.

Keywords: Arabic language; Berkeley Segmentation Database (BSD); Least Significant Bit (LSB); Roberts; Prewitt; Sobel; LoG; Canny

Anwar H. Ibrahim and Abdulrahman S. Alturki, “Computational Analysis of Arabic Cursive Steganography using Complex Edge Detection Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110959

@article{Ibrahim2020,
title = {Computational Analysis of Arabic Cursive Steganography using Complex Edge Detection Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110959},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110959},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Anwar H. Ibrahim and Abdulrahman S. Alturki}
}



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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