Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

• URL: http://arxiv.org/abs/2308.04188v1
• Date: Tue, 8 Aug 2023 11:23:56 GMT
• Title: Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch
• Authors: Yingjie He, Yuanman Li, Changsheng Chen and Xia Li
• Abstract summary: We propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions.
• Score: 17.67927506844985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.

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