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.gitattributes

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README.md

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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/val_LF-VSN_1video_celebA.yml   # predicted tamper masks
+python test.py  -opt options/test/photoshop.yml                  # ground-truth masks
+```
+
+## 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"
+}
+```

models/200000_G.pth

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training_state/200000.state

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