- Title
- Deep learning-based digital image forgery detection using transfer learning
- Creator
- Qazi, Emad; Zia, Tanveer; Imran, Muhammad; Faheem, Muhammad
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199899
- Identifier
- vital:19287
- Identifier
-
https://doi.org/10.32604/iasc.2023.041181
- Identifier
- ISSN:1079-8587 (ISSN)
- Abstract
- Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training. Therefore, in this study, a transfer learning based deep learning technique for image forgery detection is proposed. The proposed methodology consists of three modules namely; preprocessing module, convolutional module, and the classification module. By using our proposed technique, the training time is drastically reduced by utilizing the pre-trained weights. The performance of the proposed technique is evaluated by using benchmark datasets, i.e., BOW and BOSSBase that detect five forensic types which include JPEG compression, contrast enhancement (CE), median filtering (MF), additive Gaussian noise, and resampling. We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%. The results show the superiority of the proposed system. © 2023, Tech Science Press. All rights reserved.
- Publisher
- Tech Science Press
- Relation
- Intelligent Automation and Soft Computing Vol. 38, no. 3 (2023), p. 225-240
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023, Tech Science Press
- Rights
- Open Access
- Subject
- 46 Information and computing sciences; 40 Engineering; BOSSBase dataset; BOW dataset; Deep learning; Image forgery; Transfer learning
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