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title: Deepfake Detect
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sdk: gradio
app_file: app.py
pinned: false
license: gpl-3.0
sdk_version: 4.37.2
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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 in order to increase the detector robustness to compression and resizing.
Run the detector
Prerequisites
- Create and activate the conda environment
conda env create -f environment.yml
conda activate gan-image-detection
- Download the model's weights from this link and unzip the file under the main folder
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.
python gan_vs_real_detector.py --img_path $PATH_TO_TEST_IMAGE
Performance
We provide a notebook 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:
@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
- Sara Mandelli
- Nicolò Bonettini
- Paolo Bestagini
- Stefano Tubaro