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DOI: 10.14569/IJACSA.2022.0131021
PDF

Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images

Author 1: Bashir Idris
Author 2: Lili N. Abdullah
Author 3: Alfian Abdul Halim
Author 4: Mohd Taufik Abdullah Selimun

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis. Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images. It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image. This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives). CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison. The edge detection operators were implemented with their respective convolution masks. Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks. These masks determine the quality of the detected edges. Edges reflect a great-intensity contrast that is either darker or brighter.

Keywords: Edge detection; first derivative; second derivatives; robert; sobel; prewitt; laplacian; canny edge detector

Bashir Idris, Lili N. Abdullah, Alfian Abdul Halim and Mohd Taufik Abdullah Selimun, “Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131021

@article{Idris2022,
title = {Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131021},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131021},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Bashir Idris and Lili N. Abdullah and Alfian Abdul Halim and Mohd Taufik Abdullah Selimun}
}



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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