Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification

Author:

Nagm Ahmad M.1,Moussa Mona M.2,Shoitan Rasha2,Ali Ahmed34ORCID,Mashhour Mohamed15,Salama Ahmed S.16ORCID,AbdulWakel Hamada I.5ORCID

Affiliation:

1. Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, Cairo, Egypt

2. Computer and Systems Department, Electronics Research Institute, Cairo, Egypt

3. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

4. Computer Science, Higher Future Institute for Specialized Technological Studies, Cairo, Egypt

5. Computer Science Department, Faculty of Computers and Information, Minia University, Minia, Egypt

6. Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, Egypt

Abstract

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

Funder

Prince Sattam bin Abdulaziz University

Publisher

PeerJ

Reference49 articles.

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