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.

翻译：近期发展的深度算法在图像复制-移动伪造检测（CMFD）领域取得了显著进展，但在某些实际场景中泛化能力有限——例如复制-移动对象未出现在训练图像中，或克隆区域来自背景。针对上述问题，本文通过融合传统方法和深度方法的优势，提出了一种新颖的端到端CMFD框架。具体而言，我们设计了一种专门针对CMFD的深度跨尺度PatchMatch方法，用于定位复制-移动区域。与现有深度模型不同，本方案旨在利用高分辨率尺度提取的特征，在源区域与目标区域之间寻找明确且可靠的逐点匹配。此外，我们开发了一个篡改区域定位分支以实现源/目标分离。所提出的CMFD框架完全可微，能够以端到端方式进行训练。大量实验结果表明，本方法对不同复制-移动内容具有高泛化能力，且性能显著优于现有方法。