This is the implementation of the method proposed in "ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization" with Pytorch(1.9.0 + cu102). The aim of this repository is to achieve robust image tampering localization.

• codes
  • models: codes of SCSEUnet ^[1]
  • MVSS_net: codes of MVSSNet ^[2]
  • denseFCN.py: code of DFCN ^[3]
  • SCUNet_main: codes of SCUNet ^[4]
  • metrics.py: code for computing the localization performance.
  • test.py: the testing script.
  • train.py: the training script.
  • configs.py: the config of training ReLoc.
• checkpoints: the weights of ReLoc equipped with 3 localization modules (i.e., DFCN, SCSEUnet, and MVSSNet) trained on DEFACTO dataset. You can download these files from Baidu Yun (Code: e5ww)

The tampering localization methods and restoration method used in this paper can find in the following links:

• SCSE-Unet ^[1]: paper and codes
• MVSSNet ^[2]: paper and codes
• DFCN ^[3]: paper and codes
• SCUnet ^[4]: paper and codes

If you use our code please cite:

@ARTICLE{ReLoc,

title={ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization},

author={Zhuang, Peiyu and Li, Haodong and Yang, Rui and Huang, Jiwu},

journal={IEEE Transactions on Information Forensics and Security},

year={2023},

volume={18},

pages={5243-5257}}