EFFECTIVE IMAGE TAMPERING LOCALIZATION VIA ENHANCED TRANSFORMER AND CO-ATTENTION FUSION

Network Architecture

Update

• 21.04.26. We updated the weight which can be downloaded from Google Drive Link or Baiduyun Link (password：EITL) and the file nets/EITLnet.py. The latest corrected experimental results are marked in red in the table below, which the average performance is more higher than before(paper ).

Environment

• Python 3.8
• cuda11.1+cudnn8.0.4

Requirements

• pip install requirements.txt

Training datasets

The training dataset catalog is as follows. The mask image in the folder has only two values of 0 and 1.

├─train_dataset
    ├─ImageSets
    │  └─Segmentation
    │          train.txt
    │          val.txt
    ├─JPEGImages
    │      00001.jpg
    │      00002.jpg
    │      00003.jpg     
    │      ...
    └─SegmentationClass
            00001_gt.png
            00002_gt.png
            00003_gt.png

Trained Models

Please download the weight from Google Drive Link or Baiduyun Link(password:EITL) and place it in the weights/ directory.

Training

python train.py

Testing

python test.py

Bibtex

@inproceedings{guo2023effective,
 title={Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion},
 author={Guo, Kun and Zhu, Haochen and Cao, Gang},
 booktitle={ICASSP},
 year={2024}
}

Contact

If you have any questions, please contact me(guokun21@qq.com).