README.md

---
license: mit
pipeline_tag: image-to-image
tags:
- watermarking
- image-self-recovery
- tamper-localization
- invertible-neural-network
---

# ReImage: Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling

Official pretrained weights for **ReImage**, a neural watermarking-based image self-recovery framework that embeds a shuffled version of the target image into itself as a watermark, enabling tamper localization and recovery of the original content.

- 📄 Paper: [arXiv:2511.22936](https://arxiv.org/abs/2511.22936)
- 🌐 Project page: https://eurominyoung186.github.io/ReImage/
- 💻 Code: https://github.com/EuroMinyoung186/ReImage

## Files

| File | Description |
|---|---|
| `models/200000_G.pth` | Network weights at iteration 200k (use this for inference) |
| `training_state/200000.state` | Optimizer/scheduler state at iteration 200k (only needed to resume training) |

## Download

```bash
pip install -U huggingface_hub
hf download Eurong2/ReImage --local-dir ../experiments/invertible_pipeline
```

The repo mirrors the directory layout expected by the test configs (an `experiments/` directory next to the code repository root):

```
../experiments/invertible_pipeline/models/200000_G.pth
../experiments/invertible_pipeline/training_state/200000.state
```

Then run inference following the instructions in the code repository:

```bash
python test2.py -opt options/test/test.yml
```

## Citation

```bibtex
@article{kim2025reimage,
    title = "Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling",
    author = "Kim, Minyoung and Seo, Paul Hongsuck",
    journal = "arXiv preprint arXiv:2511.22936",
    year = "2025"
}
```