MAGritte in PyTorch
Image manipulation detection and localization using Mixed Adversarial Generators. Code for the paper The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection, NIPS 2019.
The code was written by Vladimir V. Kniaz and SolidHuman.
Note: The current software works well with PyTorch 1.2+ and Python 3.7+.
Prerequisites
- Linux or macOS
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
Getting Started
Installation
- Clone this repo:
git clone https://github.com/vlkniaz/MAGritte
cd MAGritte
- Install PyTorch 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
- For pip users, please type the command
pip install -r requirements.txt
. - For Conda users, we provide a installation script
./scripts/conda_deps.sh
. Alternatively, you can create a new Conda environment usingconda env create -f environment.yml
.
- For pip users, please type the command
MAGritte train/test
- Download a fantastic_reality dataset:
bash ./datasets/download_fantastic_reality_dataset.sh
- Train a model:
bash ./scripts/train_magritte_edge.sh
-
To view training results and loss plots, run
python -m visdom.server
and click the URL http://localhost:8097. To see more intermediate results, check out./checkpoints/magritte_magritte_edge/web/index.html
. -
Test the model:
bash ./scripts/test_magritte_edge.sh
- The test results will be saved to a html file here:
./results/magritte_magritte_edge/test_latest/index.html
.
Datasets
Download MAGritte dataset and create your own datasets.
Citation
If you use this code for your research, please cite our papers.
@inproceedings{MAG2019
...
}
Related Projects
ManTraNet | Fighting Fake News
Acknowledgments
Our code is based on pix2pix.