Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model
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 cluste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-11-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2022-0041 |
| _version_ | 1811319866732314624 |
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| author | Tinnathi Sreenivasu Sudhavani G. |
| author_facet | Tinnathi Sreenivasu Sudhavani G. |
| author_sort | Tinnathi Sreenivasu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T12:49:51Z |
| format | Article |
| id | doaj.art-07423734595541dba28ae5be4c334798 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-04-13T12:49:51Z |
| publishDate | 2022-11-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-07423734595541dba28ae5be4c3347982022-12-22T02:46:14ZengSciendoCybernetics and Information Technologies1314-40812022-11-012249111010.2478/cait-2022-0041Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet ModelTinnathi Sreenivasu0Sudhavani G.1Department of ECE, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IndiaDepartment of ECE, R.V.R & J.C College of Engineering, Guntur, Andhra Pradesh, IndiaIn 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.https://doi.org/10.2478/cait-2022-0041adaptive matching algorithmalexnetcopy-move forgery detectiongrey wolf optimizersuperpixel clustering algorithm |
| spellingShingle | Tinnathi Sreenivasu Sudhavani G. Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model Cybernetics and Information Technologies adaptive matching algorithm alexnet copy-move forgery detection grey wolf optimizer superpixel clustering algorithm |
| title | Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model |
| title_full | Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model |
| title_fullStr | Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model |
| title_full_unstemmed | Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model |
| title_short | Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model |
| title_sort | copy move forgery detection using superpixel clustering algorithm and enhanced gwo based alexnet model |
| topic | adaptive matching algorithm alexnet copy-move forgery detection grey wolf optimizer superpixel clustering algorithm |
| url | https://doi.org/10.2478/cait-2022-0041 |
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