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

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 combatin...

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Bibliographic Details
Published in:	PeerJ. Computer science Vol. 10; p. e2205
Main Authors:	Nagm, Ahmad M, Moussa, Mona M, Shoitan, Rasha, Ali, Ahmed, Mashhour, Mohamed, Salama, Ahmed S, AbdulWakel, Hamada I
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 07-08-2024 PeerJ Inc
Subjects:	Algorithms Algorithms and Analysis of Algorithms Analysis CNN Computer programs Copy-move Data Mining and Machine Learning ELA Forgery Graphics software Image processing Image splicing Neural Networks Security and Privacy Tampering Image splicing CNN Forgery ELA Copy-move Tampering
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2376-5992 2376-5992
DOI:	10.7717/peerj-cs.2205