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.

翻译：深度学习的近期进展在图像复制-移动伪造检测（CMFD）领域取得了显著成效。然而，现有算法在复制区域未出现在训练图像中或克隆区域属于背景部分的实际场景中缺乏泛化能力。此外，这些算法利用卷积运算区分源区域与目标区域，导致当目标区域与背景良好融合时检测效果不佳。为解决上述局限，本研究提出一种融合传统方法与深度学习优势的新型端到端CMFD框架。具体而言，研究开发了一种针对CMFD定制的深度跨尺度块匹配（PM）方法，用于定位复制-移动区域。与现有深度模型不同，本文方法利用高分辨率尺度提取的特征，在源区域与目标区域之间建立显式且可靠的逐点匹配。进一步地，我们提出一种新型成对排序学习框架来分离源区域与目标区域。通过利用逐点匹配的强先验信息，该框架能够识别细微差异并有效区分源区域与目标区域，即使目标区域与背景高度融合时仍可奏效。所提框架完全可微，支持端到端训练。全面的实验结果表明，本方案在各种复制-移动场景中展现出卓越的泛化能力，显著优于现有方法。