Trusted Video Inpainting Localization via Deep Attentive Noise Learning

An official implementation code for paper "Trusted Video Inpainting Localization via Deep Attentive Noise Learning". This repo provides code and trained weights.

Framework

Dependency

• torch 1.7.0
• python 3.7

Datasets

1. DAVIS2016
2. DAVIS2017
3. MOSE
4. VOS2k5-800 (in this paper we use 800 videos from VOS2k5)

The MOSE100 dataset in this paper can be found in this

Video inpainting algorithms

1. VI
2. OP
3. CP
4. E2FGVI
5. FuseFormer
6. STTN
7. FGT
8. FGVC
9. ISVI

Usage

For example to train:

python train.py

For example to test: download TruVIL_train_VI_OP.pth and place it in checkpoints directory.

python test.py

For example to inference: download TruVIL_train_VI_OP.pth and place it in checkpoints directory.

python inference.py

Citation

If you use this code for your research, please cite our paper

@article{lou2025trusted,
  title={Trusted Video Inpainting Localization via Deep Attentive Noise Learning},
  author={Lou, Zijie and Cao, Gang and Lin, Man and Yu, Lifang and Weng, Shaowei},
  journal={IEEE Transactions on Dependable and Secure Computing},
  year={2025},
  publisher={IEEE}
}

License

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.