首页|Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

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  • NETL
  • NSTL
  • IEEE
Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods.

Feature extractionForgeryTransformsTrainingSecurityDetection algorithmsSemanticsReviewsObject recognitionImage segmentation

Yuanman Li、Yingjie He、Changsheng Chen、Li Dong、Bin Li、Jiantao Zhou、Xia Li

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Guangdong Provincial Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

Department of Computer Science, Ningbo University, Ningbo, China

Department of Computer and Information Science, University of Macau, Macau, China

2025

10.1109/TIP.2024.3482191

IEEE transactions on image processing

IEEE transactions on image processing

ISSN：
年,卷(期)：2025.34(1)
  • 43