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title: Deepfake Detect | |
emoji: 📈 | |
colorFrom: indigo | |
colorTo: pink | |
sdk: gradio | |
app_file: app.py | |
pinned: false | |
license: gpl-3.0 | |
sdk_version: 4.37.2 | |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference | |
# GAN-image-detection | |
This repository contains a GAN-generated image detector developed to distinguish real images from synthetic ones. | |
The detector is based on an ensemble of CNNs. | |
The backbone of each CNN is the EfficientNet-B4. | |
Each model of the ensemble has been trained in a different way following the suggestions presented in [this paper](https://ieeexplore.ieee.org/abstract/document/9360903) in order to increase the detector robustness to compression and resizing. | |
## Run the detector | |
### Prerequisites | |
1. Create and activate the conda environment | |
```bash | |
conda env create -f environment.yml | |
conda activate gan-image-detection | |
``` | |
2. Download the model's weights from [this link](https://www.dropbox.com/s/g1z2u8wl6srjh6v/weigths.zip) and unzip the file under the main folder | |
```bash | |
wget https://www.dropbox.com/s/g1z2u8wl6srjh6v/weigths.zip | |
unzip weigths.zip | |
``` | |
### Test the detector on a single image | |
We provide a simple script to obtain the model score for a single image. | |
```bash | |
python gan_vs_real_detector.py --img_path $PATH_TO_TEST_IMAGE | |
``` | |
## Performance | |
We provide a [notebook](https://github.com/polimi-ispl/GAN-image-detection/blob/main/roc_curves.ipynb) with the script for computing the ROC curve for each dataset. | |
## How to cite | |
Training procedures have been carried out following the suggestions presented in the following paper. | |
Plaintext: | |
``` | |
S. Mandelli, N. Bonettini, P. Bestagini, S. Tubaro, "Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision", IEEE International Workshop on Information Forensics and Security (WIFS), 2020, doi: 10.1109/WIFS49906.2020.9360903. | |
``` | |
Bibtex: | |
```bibtex | |
@INPROCEEDINGS{mandelli2020training, | |
author={Mandelli, Sara and Bonettini, Nicolò and Bestagini, Paolo and Tubaro, Stefano}, | |
booktitle={IEEE International Workshop on Information Forensics and Security (WIFS)}, | |
title={Training {CNNs} in Presence of {JPEG} Compression: Multimedia Forensics vs Computer Vision}, | |
year={2020}, | |
doi={10.1109/WIFS49906.2020.9360903}} | |
``` | |
## Credits | |
[Image and Sound Processing Lab - Politecnico di Milano](http://ispl.deib.polimi.it/) | |
- Sara Mandelli | |
- Nicolò Bonettini | |
- Paolo Bestagini | |
- Stefano Tubaro |