Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?

• URL: http://arxiv.org/abs/2305.10247v1
• Date: Wed, 17 May 2023 14:35:56 GMT
• Title: Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?
• Authors: Shizhen Chang
• Abstract summary: Copy-move forgery detection is a crucial research area within digital image forensics. Recent years have witnessed an increased interest in distinguishing between the original and duplicated objects in copy-move forgeries. We propose an innovative method that employs the transformer architecture in an end-to-end deep neural network.
• Score: 3.7311680121118345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purposes. Recent years have witnessed an increased interest in distinguishing between the original and duplicated objects in copy-move forgeries, accompanied by the development of larger-scale datasets to facilitate this task. However, existing approaches to copy-move forgery detection and source/target differentiation often involve two separate steps or the design of individual end-to-end networks for each task. In this paper, we propose an innovative method that employs the transformer architecture in an end-to-end deep neural network. Our method aims to detect instances of copy-move forgery while simultaneously localizing the source and target regions. By utilizing this approach, we address the challenges posed by multi-object copy-move scenarios and report if there is a balance between the detection and differentiation tasks. To evaluate the performance of our proposed network, we conducted experiments on two publicly available copy-move datasets. The results and analysis aims to show the potential significance of our focus in balancing detection and distinguishment result and transferring the trained model in different datasets in the field.

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