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.

该研究提出了一种新颖的端到端图像复制移动伪造检测框架，它结合了传统和深度学习方法的优势，利用高分辨率尺度提取特征进行源和目标区域的明确可靠点对点匹配，并提出了一种新型的成对排序学习框架来分离源和目标区域，取得了显著的泛化能力和优于现有方法的检测性能。