We propose a method to identify the source and target regions of a copy-move forgery so allow a correct localisation of the tampered area. First, we cast the problem into a hypothesis testing framework whose goal is to decide which region between the two nearly-duplicate regions detected by a generic copy-move detector is the original one. Then we design a multi-branch CNN architecture that solves the hypothesis testing problem by learning a set of features capable to reveal the presence of interpolation artefacts and boundary inconsistencies in the copy-moved area. The proposed architecture, trained on a synthetic dataset explicitly built for this purpose, achieves good results on copy-move forgeries from both synthetic and realistic datasets. Based on our tests, the proposed disambiguation method can reliably reveal the target region even in realistic cases where an approximate version of the copy-move localization mask is provided by a state-of-the-art copy-move detection algorithm.

中文翻译：

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通过多分支 CNN 进行复制移动源-目标消歧


我们提出了一种方法来识别复制移动伪造的源区域和目标区域，以便正确定位被篡改的区域。首先，我们将问题放入假设检验框架中，其目标是确定通用复制移动检测器检测到的两个几乎重复的区域之间的哪个区域是原始区域。然后，我们设计了一个多分支 CNN 架构，通过学习一组能够揭示复制移动区域中插值伪影和边界不一致的存在的特征来解决假设检验问题。所提出的架构在专门为此目的构建的合成数据集上进行训练，在来自合成数据集和真实数据集的复制移动伪造方面取得了良好的结果。根据我们的测试，即使在由最先进的复制移动检测算法提供复制移动定位掩码的近似版本的实际情况下，所提出的消歧方法也可以可靠地揭示目标区域。