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DOI: 10.14569/IJACSA.2017.080516
PDF

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Author 1: Dr. Diaa Mohammed Uliyan
Author 2: Dr. Mohammed A. F. Al-Husainy

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 5, 2017.

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Abstract: Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.

Keywords: Digital image forensics; Region duplication; Forgery detection; Image authentication

Dr. Diaa Mohammed Uliyan and Dr. Mohammed A. F. Al-Husainy. “Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature”. International Journal of Advanced Computer Science and Applications (IJACSA) 8.5 (2017). http://dx.doi.org/10.14569/IJACSA.2017.080516

@article{Uliyan2017,
title = {Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080516},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080516},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Dr. Diaa Mohammed Uliyan and Dr. Mohammed A. F. Al-Husainy}
}



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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