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. 2024 Aug 7:10:e2205.
doi: 10.7717/peerj-cs.2205. eCollection 2024.

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

Ahmad M Nagm  1 , Mona M Moussa  2 , Rasha Shoitan  2 , Ahmed Ali  3   4 , Mohamed Mashhour  1   5 , Ahmed S Salama  1   6 , Hamada I AbdulWakel  5
Affiliations

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

Ahmad M Nagm et al. PeerJ Comput Sci. .

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.

Keywords: CNN; Copy-move; ELA; Forgery; Image splicing; Tampering.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. The proposed image forgery framework.
Figure 2
Figure 2. The proposed CNN model architecture.
Figure 3
Figure 3. Successfully detecting authentic images.
(A) Original images. (B) The corresponding ELA images.
Figure 4
Figure 4. Successfully detecting forged images.
(A) Forged images. (B) The corresponding ELA. (C) The corresponding ground truth.
Figure 5
Figure 5. Misclassified forged images.
(A) Forged images. (B) The extracted ELA. (C) The corresponding ground truth.
See this image and copyright information in PMC

References

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