An Evaluation of Convolutional Neural Network (CNN) Model for Copy-Move and Splicing Forgery Detection
Image forgeries such as copy-move and splicing are very common due to the availability of the advancement in software editing techniques. However, most of the existing methods for forgery detection consider only one type of image forgery due to the reason that both forgeries have different traits....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IJISAE
2023
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| Online Access: | http://eprints.uthm.edu.my/9251/ http://eprints.uthm.edu.my/9251/1/J16045_03bd2e85e9c5ccb6bf013a56ac2dba37.pdf |
| _version_ | 1848889625165168640 |
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| author | Muniappan, Thiiban Abd Warif, Nor Bakiah Ismail, Ahsiah Mat Abir, Noor Atikah |
| author_facet | Muniappan, Thiiban Abd Warif, Nor Bakiah Ismail, Ahsiah Mat Abir, Noor Atikah |
| author_sort | Muniappan, Thiiban |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Image forgeries such as copy-move and splicing are very common due to the availability of the advancement in
software editing techniques. However, most of the existing methods for forgery detection consider only one type of image forgery due to the reason that both forgeries have different traits. In this paper, a Convolutional Neural Network (CNN) model which is one of the deep learning approaches is simulated and analyzed to detect any forged image without knowing their types of forgeries. In the model, three phases are involved: Data Preprocessing, Feature Extraction, and Classification. The
model learns to extract features from convolutional, pooling, and Rectified Linear unit layer, and classified the image whether
it is original or forged using fully connected layer. For the experimental works, three datasets namely MICC-F2000 (2000
images), CASIA 1 (1721 images), and CASIA 2 (12615 images) are tested and compared with existing deep learning-based
methods. The results show that the CNN model achieved the highest performance with accuracy of 79% for CASIA 1 and
89% for CASIA 2. |
| first_indexed | 2025-11-15T20:29:09Z |
| format | Article |
| id | uthm-9251 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:29:09Z |
| publishDate | 2023 |
| publisher | IJISAE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-92512023-07-17T07:46:07Z http://eprints.uthm.edu.my/9251/ An Evaluation of Convolutional Neural Network (CNN) Model for Copy-Move and Splicing Forgery Detection Muniappan, Thiiban Abd Warif, Nor Bakiah Ismail, Ahsiah Mat Abir, Noor Atikah T Technology (General) Image forgeries such as copy-move and splicing are very common due to the availability of the advancement in software editing techniques. However, most of the existing methods for forgery detection consider only one type of image forgery due to the reason that both forgeries have different traits. In this paper, a Convolutional Neural Network (CNN) model which is one of the deep learning approaches is simulated and analyzed to detect any forged image without knowing their types of forgeries. In the model, three phases are involved: Data Preprocessing, Feature Extraction, and Classification. The model learns to extract features from convolutional, pooling, and Rectified Linear unit layer, and classified the image whether it is original or forged using fully connected layer. For the experimental works, three datasets namely MICC-F2000 (2000 images), CASIA 1 (1721 images), and CASIA 2 (12615 images) are tested and compared with existing deep learning-based methods. The results show that the CNN model achieved the highest performance with accuracy of 79% for CASIA 1 and 89% for CASIA 2. IJISAE 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9251/1/J16045_03bd2e85e9c5ccb6bf013a56ac2dba37.pdf Muniappan, Thiiban and Abd Warif, Nor Bakiah and Ismail, Ahsiah and Mat Abir, Noor Atikah (2023) An Evaluation of Convolutional Neural Network (CNN) Model for Copy-Move and Splicing Forgery Detection. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING, 11 (1). pp. 730-740. ISSN 2147-679921 |
| spellingShingle | T Technology (General) Muniappan, Thiiban Abd Warif, Nor Bakiah Ismail, Ahsiah Mat Abir, Noor Atikah An Evaluation of Convolutional Neural Network (CNN) Model for Copy-Move and Splicing Forgery Detection |
| title | An Evaluation of Convolutional Neural Network (CNN) Model
for Copy-Move and Splicing Forgery Detection |
| title_full | An Evaluation of Convolutional Neural Network (CNN) Model
for Copy-Move and Splicing Forgery Detection |
| title_fullStr | An Evaluation of Convolutional Neural Network (CNN) Model
for Copy-Move and Splicing Forgery Detection |
| title_full_unstemmed | An Evaluation of Convolutional Neural Network (CNN) Model
for Copy-Move and Splicing Forgery Detection |
| title_short | An Evaluation of Convolutional Neural Network (CNN) Model
for Copy-Move and Splicing Forgery Detection |
| title_sort | evaluation of convolutional neural network (cnn) model
for copy-move and splicing forgery detection |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9251/ http://eprints.uthm.edu.my/9251/1/J16045_03bd2e85e9c5ccb6bf013a56ac2dba37.pdf |