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Open Access

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

University College of Engineering, JNTU Kakinada, Kakinada 533003, India.
Department of ECE, JNTUK UCE, Vizianagaram 535003, India.
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Abstract

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Keywords

copy move forgery detectionimage authenticationpassive image forgery detectionblind forgery detection

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Big Data Mining and Analytics
Volume 6 Issue 3,
September 2023
Pages 347-360
DOI: 10.26599/BDMA.2022.9020029
Cite this article:
Babu SBGT, Rao CS. Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers. Big Data Mining and Analytics, 2023, 6(3): 347-360. https://doi.org/10.26599/BDMA.2022.9020029

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Received: 02 March 2022
Revised: 27 June 2022
Accepted: 25 July 2022
Published: 07 April 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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