Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection
Copy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content. Detection of such forgeries acts as an important part of digital image forensics. Deep learning techniques, such as convolutional neural networks...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10214271/ |
| _version_ | 1827863015757709312 |
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| author | Mashael Maashi Hayam Alamro Heba Mohsen Noha Negm Gouse Pasha Mohammed Noura Abdelaziz Ahmed Sara Saadeldeen Ibrahim Mohamed Ibrahim Alsaid |
| author_facet | Mashael Maashi Hayam Alamro Heba Mohsen Noha Negm Gouse Pasha Mohammed Noura Abdelaziz Ahmed Sara Saadeldeen Ibrahim Mohamed Ibrahim Alsaid |
| author_sort | Mashael Maashi |
| collection | DOAJ |
| description | Copy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content. Detection of such forgeries acts as an important part of digital image forensics. Deep learning techniques, such as convolutional neural networks (CNNs), are employed to extract informative features from images. CNNs are known for their ability to capture complex patterns and structures, making them well-suited for image-related tasks like forgery detection. This paper introduces a reptile search algorithm with a deep transfer learning-based CM forgery detection (RSADTL-CMFD) approach. The presented model uses Neural Architectural Search Network (NASNet) for feature extraction in forgery detection which allows the network to effectively capture relevant and discriminative features from the input images. To enhance the performance of the NASNet model, we employ the reptile search algorithm (RSA) for hyperparameter tuning. This algorithm optimizes the network’s hyperparameters, enabling the model to quickly adapt to different forgery detection tasks and achieve superior performance. Finally, extreme gradient boosting (XGBoost) effectively utilizes the extracted features from the deep learning network to classify regions within the image as genuine or manipulated/forged. The experimental result analysis of the RSADTL-CMFD model is tested using benchmark datasets. An extensive comparative study highlighted the enhanced outcomes of the RSADTL-CMFD method over recent techniques. |
| first_indexed | 2024-03-12T14:02:49Z |
| format | Article |
| id | doaj.art-83b01fd82956458ab484a2b53577e3b2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:02:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-83b01fd82956458ab484a2b53577e3b22023-08-21T23:00:35ZengIEEEIEEE Access2169-35362023-01-0111872978730410.1109/ACCESS.2023.330423710214271Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery DetectionMashael Maashi0https://orcid.org/0000-0003-0446-5430Hayam Alamro1https://orcid.org/0000-0003-3157-8086Heba Mohsen2Noha Negm3https://orcid.org/0009-0005-5911-1033Gouse Pasha Mohammed4https://orcid.org/0000-0003-1583-9872Noura Abdelaziz Ahmed5Sara Saadeldeen Ibrahim6Mohamed Ibrahim Alsaid7Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, EgyptDepartment of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaCopy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content. Detection of such forgeries acts as an important part of digital image forensics. Deep learning techniques, such as convolutional neural networks (CNNs), are employed to extract informative features from images. CNNs are known for their ability to capture complex patterns and structures, making them well-suited for image-related tasks like forgery detection. This paper introduces a reptile search algorithm with a deep transfer learning-based CM forgery detection (RSADTL-CMFD) approach. The presented model uses Neural Architectural Search Network (NASNet) for feature extraction in forgery detection which allows the network to effectively capture relevant and discriminative features from the input images. To enhance the performance of the NASNet model, we employ the reptile search algorithm (RSA) for hyperparameter tuning. This algorithm optimizes the network’s hyperparameters, enabling the model to quickly adapt to different forgery detection tasks and achieve superior performance. Finally, extreme gradient boosting (XGBoost) effectively utilizes the extracted features from the deep learning network to classify regions within the image as genuine or manipulated/forged. The experimental result analysis of the RSADTL-CMFD model is tested using benchmark datasets. An extensive comparative study highlighted the enhanced outcomes of the RSADTL-CMFD method over recent techniques.https://ieeexplore.ieee.org/document/10214271/Cybersecurityimage forgerycopy move detectionmachine learningdeep learningparameter optimization |
| spellingShingle | Mashael Maashi Hayam Alamro Heba Mohsen Noha Negm Gouse Pasha Mohammed Noura Abdelaziz Ahmed Sara Saadeldeen Ibrahim Mohamed Ibrahim Alsaid Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection IEEE Access Cybersecurity image forgery copy move detection machine learning deep learning parameter optimization |
| title | Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection |
| title_full | Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection |
| title_fullStr | Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection |
| title_full_unstemmed | Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection |
| title_short | Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection |
| title_sort | modeling of reptile search algorithm with deep learning approach for copy move image forgery detection |
| topic | Cybersecurity image forgery copy move detection machine learning deep learning parameter optimization |
| url | https://ieeexplore.ieee.org/document/10214271/ |
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