Convolutional Neural Network for Copy-Move Forgery Detection

Abdalla, Younis and Iqbal, Tariq and Shehata, Mohamed S. (2019) Convolutional Neural Network for Copy-Move Forgery Detection. Symmetry, 11 (10). ISSN 2073-8994

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Digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy. In response, several approaches have been developed for detecting digital forgeries. This paper proposes a novel scheme based on neural networks and deep learning, focusing on the convolutional neural network (CNN) architecture approach to enhance a copy-move forgery detection. The proposed approach employs a CNN architecture that incorporates pre-processing layers to give satisfactory results. In addition, the possibility of using this model for various copy-move forgery techniques is explained. The experiments show that the overall validation accuracy is 90%, with a set iteration limit.

Item Type: Article
Item ID: 14602
Keywords: forgery detection, neural networks, image processing
Department(s): Engineering and Applied Science, Faculty of
Date: 14 October 2019
Date Type: Publication
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