---
task_categories:
  - image-classification
language:
  - en
pretty_name: RSFAKE-1M
size_categories:
  - 100K<n<1M
license: cc-by-nc-4.0
---

# RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries

## Dataset Summary

**RSFAKE-1M** is a large-scale dataset designed to advance the detection of forged remote sensing images, particularly those generated by diffusion models. It contains **1 million images** in total — **500K real** and **500K fake**. The forged images are produced using **10 different diffusion models** fine-tuned on remote sensing data, spanning **six generation conditions**, including text-to-image, structure-guided generation, and inpainting.

Remote sensing imagery plays a vital role in areas such as environmental monitoring, urban planning, and national security. However, with the rapid development of generative models, especially diffusion-based architectures, remote sensing images are increasingly vulnerable to realistic forgeries. Despite this, most existing benchmarks focus on GAN-based or natural image forgeries, leaving a critical gap in the remote sensing domain.

RSFAKE-1M addresses this gap by offering a comprehensive benchmark for training and evaluating forgery detection models under realistic and diverse conditions. Extensive experiments in our accompanying paper demonstrate that:

- Current state-of-the-art detectors struggle with diffusion-generated forgeries in remote sensing.
- Training on RSFAKE-1M significantly improves generalization and robustness across different forgery types.

We believe RSFAKE-1M serves as a solid foundation for the development of next-generation remote sensing forgery detection algorithms.

## Dataset Structure

```
RSFAKE/
├── FAKE/
│   ├── generated_crsdiff
│   ├── generated_diffusion_sat
│   ├── generated_diffusion_sat_256
│   ├── generated_geosynth
│   ├── generated_geosynth_canny
│   ├── generated_geosynth_sam
│   ├── generated_mapsat
│   ├── generated_rsinpaint
│   ├── generated_RSSD_768
│   ├── generated_SDFRS
│
├── REAL/
│   └── fmow/
│       ├── train/
│       ├── val/
│       └── test/
│
├── SPLIT/
│   ├── RSFAKE_train_new.csv
│   ├── RSFAKE_val_new.csv
│   └── RSFAKE_test_new.csv
```


### Real Image Construction

The **real image subset** is reconstructed from the publicly available [fMoW dataset](https://github.com/fMoW/dataset.git). To reproduce the real subset:

1. Download the original fMoW-rgb dataset to the `REAL/fmow_process/` directory.
2. Prepare the environment:

   ```bash
   pip install pillow==11.2.1 pandas==2.2.3 tqdm==4.67.1
   ```
3. Run the cropping script:

   ```bash
   cd REAL/fmow_process/
   python crop.py
   ```

The processed output will be structured into `train`, `val`, and `test` under `REAL/fmow/`.

These scripts ensure that the real image set used for RSFAKE-1M evaluation is consistent and reproducible, while respecting the original data source’s license.

## Disclaimer

RSFAKE-1M is a synthetic benchmark designed to facilitate research on forgery detection in remote sensing. The fake images are artificially generated and do **not** correspond to real-world scenes or locations. They must **not** be used for any purpose that could mislead, misinform, or be interpreted as real satellite or aerial data.

All model-generated content is based on publicly available generative models listed below. RSFAKE-1M does not distribute or modify the original models themselves — only images produced under fair-use conditions are included.

By using this dataset, you agree:

* The **real images** are reconstructed from the publicly available FMoW dataset and remain subject to its original license.
* The **forged images** are generated using publicly available diffusion models, whose licenses we fully acknowledge.
* We do **not claim ownership** of any third-party models or datasets used in RSFAKE-1M.
* This dataset is provided **strictly for non-commercial research and educational use**.
* Users must **cite the RSFAKE-1M paper** and comply with the licenses of all referenced resources.
* The authors bear **no responsibility for any misuse or downstream consequences** related to this dataset.



  
## Citation

```
@misc{tan2025rsfake1mlargescaledatasetdetecting,
      title={RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries},
      author={Zhihong Tan and Jiayi Wang and Huiying Shi and Binyuan Huang and Hongchen Wei and Zhenzhong Chen},
      year={2025},
      eprint={2505.23283},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.23283},
}
```

## Acknowledgements

We would like to thank the creators of the following models and datasets, which served as the basis for generating the fake and real images in RSFAKE-1M:

- 🛰 **Real Image Source**

  - [fMoW (Functional Map of the World)](https://github.com/fMoW/dataset.git) *(FUNCTIONAL MAP OF THE WORLD CHALLENGE PUBLIC LICENSE)*

- 🧨 **Diffusion-Based Generative Models**

  - [CRSDiff](https://github.com/Sonettoo/CRS-Diff.git) 
  - [Diffusion-SAT](https://github.com/samar-khanna/DiffusionSat) *(Apache 2.0)*
  - [GeoSynth](https://github.com/mvrl/GeoSynth.git) *(Apache 2.0)*
  - [MapSat](https://github.com/miquel-espinosa/map-sat.git) *(Apache 2.0)*
  - [RSPaint](https://github.com/SteveImmanuel/rs-paint.git) *(Apache 2.0)*
  - [SDFRS](https://github.com/xiaoyuan1996/Stable-Diffusion-for-Remote-Sensing-Image-Generation.git) *(MIT License)*
  - [GeoRSSD](https://huggingface.co/Zilun/GeoRSSD) *(Apache 2.0)*

We sincerely acknowledge the contributions of the above works. This dataset would not have been possible without their efforts in advancing generative modeling in the remote sensing domain.