# HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation

arXiv: https://arxiv.org/abs/2307.11052 · HTML: https://mlsanity.com/paper/2307.11052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images](https://mlsanity.com/paper/1905.06368/) (2019)
- [ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images](https://mlsanity.com/paper/2112.03553/) (2021)
- [Manipulation Detection in Satellite Images Using Vision Transformer](https://mlsanity.com/paper/2105.06373/) (2021)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [Manipulation Detection in Satellite Images Using Deep Belief Networks](https://mlsanity.com/paper/2004.12441/) (2020)
- [Splicing Detection and Localization In Satellite Imagery Using Conditional GANs](https://mlsanity.com/paper/2205.01805/) (2022)

## Abstract (arXiv)

Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such as inaccurate boundaries between pristine and forged regions, the generation of unwanted artifacts, etc. To tackle the aforementioned challenges, inspired by the high-resolution image segmentation literature, we propose a novel model called HRFNet to enable satellite image forgery localization effectively. Specifically, equipped with shallow and deep branches, our model can successfully integrate RGB and resampling features in both global and local manners to localize forgery more accurately. We perform various experiments to demonstrate that our method achieves the best performance, while the memory requirement and processing speed are not compromised compared to existing methods.