Spatio-Temporal Co-Attention Fusion Network for Video Splicing Localization

An official implementation code for paper "Spatio-Temporal Co-Attention Fusion Network for Video Splicing Localization", JEI 2024. This repo will provide codes, trained weights, and our training dataset.

Network Architecture

Dataset

• Download the training frames from Baiduyun Link (extract code: y4vy).

Training

• Prepare for the training datasets.

└─dataset
   ├── train
   │     ├── videos
   │     │   ├── video_00000
   │     │   │    ├── 00000.png
   │     │   │    └── ...
   │     │   └── ...
   │     └── masks
   │         ├── video_00000
   │         │    ├── 00000.png
   │         │    └── ...
   │         └── ...
   └── val
         ├── videos
         │   ├── video_00000
         │   │    ├── 00000.png
         │   │    └── ...
         │   └── ...
         └── masks
             ├── video_00000
             │    ├── 00000.png
             │    └── ...
             └── ...

• Modify configuration in libs/utils/config_standard_db.py.

python train.py

Testing

Download the weights from Google Drive Link and move it into the checkpoints/.

• Run on a dataset.

python test.py

• Run on a video.

python demo.py

License

The code and dataset is released only for academic research. Commercial usage is strictly prohibited.

Citation

@article{Lin2024SCFNet,
author = {Man Lin and Gang Cao and Zijie Lou and Chi Zhang},
title = {{Spatio-temporal co-attention fusion network for video splicing localization}},
volume = {33},
journal = {Journal of Electronic Imaging},
number = {3},
publisher = {SPIE},
pages = {033027},
year = {2024},
doi = {10.1117/1.JEI.33.3.033027},
URL = {https://doi.org/10.1117/1.JEI.33.3.033027}
}

Contact

If you have any questions, please contact me([email protected]).