Model Parsing (IEEE TPAMI)
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.
[2023/07/29] Our Paper is accepted to Transactions on Pattern Analysis and Machine Intelligence!!
[2023/07/29] The version 3 of the code is released! The old codebase is deleted. Main branch represents version 3.
[2022/06/30] The version 2 of the code is released!
[2021/06/15] In collaboration with Meta AI, our model parsing work is widely reported in CNBC, CNET, Engadget, Fortune, The Mac Observer, MSU Today, New Scientist, SiliconAngle, VentureBeat, The Verge, and Wall Street Journal.
[2021/05/06] Our codebase for model parsing is released is released!
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:
For reverse engineering, run:
python reverse_eng.py
For deepfake detection, run:
python deepfake_detection.py
For image attribution, run:
python image_attribution.py
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}
}