hell-to-heaven / rru-net

PyTorch implementation of RRU-Net is based on https://github.com/milesial/Pytorch-UNet

RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

This repository is for paper "RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection" (CVPR 2019 workshop)

Update (2020.12.15)

upload the pre-trained model.
NOTICING:

• the uploaded pre-trained model is trained with new datasets since i lost previous pre-trained model.
• the new dataset is produced by my new work, so i can't release it currently.

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Requirements

• Python 3.7
• PyTorch 1.0+
• CUDA 10.0+

Details

• './unet/unet-parts.py': it includes detailed implementations of 'U-Net', 'RU-Net' and 'RRU-Net'
• 'train.py': you can use it to train your model
• 'predict.py': you can use it to test

Citation

Please add following information if you cite the paper in your publication:

@inproceedings{bi2019rru,
  title={RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection},
  author={Bi, Xiuli and Wei, Yang and Xiao, Bin and Li, Weisheng},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={0--0},
  year={2019}
}

Contact yale [email protected] for any further information.