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DOI: 10.14569/IJACSA.2022.0131237
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Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm

Author 1: Anna Gustina Zainal
Author 2: Chamandeep Kaur
Author 3: Mohammed Saleh Al Ansari
Author 4: Ricardo Fernando Cosio Borda
Author 5: A. Nageswaran
Author 6: Rasha M. Abd El-Aziz

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

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Abstract: In the modern day, protecting data against tampering is a significant task. One of the most common forms of information display has been digital photographs. Images may be exploited in a variety of contexts, including the military, security applications, intelligence areas, legal evidence, social media, and journalism. Digital picture forgeries involve altering the original images with strange patterns, which result in variability in the image's characteristics. Among the most challenging forms of image forgeries to identify is Copy Move Forgery (CMF). It occurs by copying a portion or piece of the picture and then inserting it again, but in a different place. When the actual content is unavailable, techniques for detecting fake content have been utilised in image security. This study presents a novel method for Copy Move Forgery Recognition (CMFR), which is mostly based on deep learning (DL) and hybrid optimization. The hybrid Grey Wolf Optimization and African Buffalo Optimization (GWO-ABO) using Convolution Neural Network (CNN) technique i.e., GWO-ABO-CNN is the foundation of the suggested model. The developed model extracts the features of images by convolution layers, and pooling layers; hereafter, the features are matched and detect CMF. The MICC-F220, SATs-130, and MICC-F600 datasets were three publicly accessible datasets to which this methodology has been implemented. To assess the model's efficacy, the outcomes of implementing the GWO-ABO-CNN model were contrasted with those of other approaches.

Keywords: Copy move forgery; convolutional neural network; image authentication; deep learning; tampered images

Anna Gustina Zainal, Chamandeep Kaur, Mohammed Saleh Al Ansari, Ricardo Fernando Cosio Borda, A. Nageswaran and Rasha M. Abd El-Aziz, “Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131237

@article{Zainal2022,
title = {Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131237},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131237},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Anna Gustina Zainal and Chamandeep Kaur and Mohammed Saleh Al Ansari and Ricardo Fernando Cosio Borda and A. Nageswaran and Rasha M. Abd El-Aziz}
}



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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