# Copy Move Source-Target Disambiguation through Multi-Branch CNNs

arXiv: https://arxiv.org/abs/1912.12640 · HTML: https://mlsanity.com/paper/1912.12640/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://mlsanity.com/paper/2302.13004/) (2023)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering](https://mlsanity.com/paper/2102.01439/) (2021)
- [TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization](https://mlsanity.com/paper/2108.04508/) (2021)
- [Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning](https://mlsanity.com/paper/2404.17310/) (2024)
- [Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining](https://mlsanity.com/paper/2404.00611/) (2024)
- [Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?](https://mlsanity.com/paper/2305.10247/) (2023)
- [Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features](https://mlsanity.com/paper/2505.23586/) (2025)

## Abstract (arXiv)

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.