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<p>In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt &amp; pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.</p>
</abstract>

In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt & pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.

Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

Department of ECE, R.V.R & J.C College of Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based...

Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

Data publikacji: 10 lis 2022

Zakres stron: 91 - 110

Otrzymano: 31 sty 2022

Przyjęty: 25 sie 2022

DOI: https://doi.org/10.2478/cait-2022-0041

Słowa kluczowe
Adaptive Matching Algorithm, AlexNet, Copy-Move Forgery Detection, Grey Wolf Optimizer, Superpixel Clustering Algorithm

© 2022 Sreenivasu Tinnathi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

Data publikacji: 10 lis 2022

Zakres stron: 91 - 110

Otrzymano: 31 sty 2022

Przyjęty: 25 sie 2022

DOI: https://doi.org/10.2478/cait-2022-0041

Słowa kluczoweAdaptive Matching Algorithm, AlexNet, Copy-Move Forgery Detection, Grey Wolf Optimizer, Superpixel Clustering Algorithm

© 2022 Sreenivasu Tinnathi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Adaptive Matching Algorithm, AlexNet, Copy-Move Forgery Detection, Grey Wolf Optimizer, Superpixel Clustering Algorithm