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<p align=center>Model Parsing (IEEE TPAMI)</p>

Official Pytorch implementation of our T-PAMI paper "Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images".

The paper and supplementary can be found at Arxiv

Authors: Vishal Asnani, Xi Yin, Tal Hassner & Xiaoming Liu.

1. :fire: NEWS :fire:

2. Overview

<p align="center"> <img src="./image/teaser_resized.png"/> <br /> </p>

3. Training/testing

Prerequisites

Datasets

We collect a large scale dataset comprising of fake images images genearted by 116 generative models. Please visit link for more details. For reverse enginnering:

For deepfake detection:

For image_attribution:

Training

For reverse engineering, run:

python reverse_eng.py

For deepfake detection, run:

python deepfake_detection.py

For image attribution, run:

python image_attribution.py

Testing using pre-trained models

For reverse engineering, run:

python reverse_eng_test.py

For deepfake detection, run:

python deepfake_detection_test.py

For image attribution, run:

python image_attribution_test.py

If you would like to use our work, please cite:

@misc{asnani2023reverse,
      title={Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images}, 
      author={Vishal Asnani and Xi Yin and Tal Hassner and Xiaoming Liu},
      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
      year={2023}
}