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README.md

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 # GAN-image-detection
+This repository contains a GAN-generated image detector developed to distinguish real images from synthetic ones.
 
-## Prerequisite
-1. Create the conda environment
+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 [here](https://www.dropbox.com/s/g1z2u8wl6srjh6v/weigths.zip?dl=0) and unzip it under the main folder
+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?dl=0
+wget https://www.dropbox.com/s/g1z2u8wl6srjh6v/weigths.zip
 unzip weigths.zip
 ```
 
-## Test the detector on a single image
+### 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
 ```
 
-## Performances
+## 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