  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.
