Copy Move Source-Target Disambiguation through Multi-Branch CNNs

TL;DR

This paper introduces a multi-branch CNN approach to accurately identify source and target regions in copy-move forgeries, improving localization by analyzing interpolation artifacts and boundary inconsistencies.

Contribution

The paper presents a novel CNN architecture for disambiguating copy-move forgery regions, trained on synthetic data, and effective on both synthetic and real datasets.

Findings

Achieves high accuracy in source-target disambiguation

Effective on both synthetic and realistic datasets

Reliable even with approximate localization masks

Abstract

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.