Convolutional Neural Network for Copy-Move Forgery Detection

• Main Authors: Younis Abdalla, M. Tariq Iqbal, Mohamed Shehata
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Symmetry
• Subjects: forgery detection; neural networks; image processing
• Online Access: https://www.mdpi.com/2073-8994/11/10/1280

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.