Title: A novel copy-move detection and location technique based on tamper detection and similarity feature fusion

Authors: Guangyang He; Xiang Zhang; Fan Wang; Zhangjie Fu

Addresses: Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

Abstract: Copy-move is a tampering method that moves a part of the image to another area. Since the colour and brightness of the images before and after being tampered are roughly the same, it is laborious to be recognised by the human eye. To address the problem of weak feature extraction capability in current copy-move tampering detection models, this paper proposes a new image copy-move detection method. This method effectively extracts noise and edge information from the tested image through multi-angle feature fusion technology and further improves the detection performance on image tampering edges by combining dilated convolutions and attention mechanisms. In addition, the model embeds tampering detection features into similarity features, enabling similarity detection to focus on specific areas, which effectively improves the detection efficiency and accuracy of the model. Compared with existing copy-move detection methods, this method has strong robustness to various attacks while achieving good detection accuracy.

Keywords: deep learning; convolutional neural network; copy-move; image forgery detection; edge features.

DOI: 10.1504/IJAACS.2024.142523

International Journal of Autonomous and Adaptive Communications Systems, 2024 Vol.17 No.6, pp.514 - 529

Received: 12 Dec 2022
Accepted: 15 Feb 2023
Published online: 06 Nov 2024 *

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