# Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?

arXiv: https://arxiv.org/abs/2305.10247 · HTML: https://mlsanity.com/paper/2305.10247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Copy Move Source-Target Disambiguation through Multi-Branch CNNs](https://mlsanity.com/paper/1912.12640/) (2019)
- [Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks](https://mlsanity.com/paper/2304.01101/) (2023)

## Abstract (arXiv)

Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purposes. Recent years have witnessed an increased interest in distinguishing between the original and duplicated objects in copy-move forgeries, accompanied by the development of larger-scale datasets to facilitate this task. However, existing approaches to copy-move forgery detection and source/target differentiation often involve two separate steps or the design of individual end-to-end networks for each task. In this paper, we propose an innovative method that employs the transformer architecture in an end-to-end deep neural network. Our method aims to detect instances of copy-move forgery while simultaneously localizing the source and target regions. By utilizing this approach, we address the challenges posed by multi-object copy-move scenarios and report if there is a balance between the detection and differentiation tasks. To evaluate the performance of our proposed network, we conducted experiments on two publicly available copy-move datasets. The results and analysis aims to show the potential significance of our focus in balancing detection and distinguishment result and transferring the trained model in different datasets in the field.