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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"
}
```
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