Reverse_Engineering_GMs

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

The paper and supplementary can be found at https://arxiv.org/abs/2106.07873

Prerequisites

• PyTorch 1.5.0
• Numpy 1.14.2
• Scikit-learn 0.22.2

Getting Started

Datasets

For reverse enginnering:

• Download the dataset for 100 Gdenerative models from https://drive.google.com/drive/folders/1ZKQ3t7_Hip9DO6uwljZL4rYAn5viSRhu?usp=sharing
• For leave out experiment, put the training data in train folder and leave out models data in test folder
• For testing on custom images, put the data in test folder.

For deepfake detection:

• Download the CelebA/LSUN dataset

For image_attribution:

• Generate 110,000 images for four different GAN models as specified in https://github.com/ningyu1991/GANFingerprints/
• For real images, use 110,000 of CelebA dataset.
• For training: we used 100,000 images and remaining 10,000 for testing.

Training

• Provide the train and test path in respective codes as sepecified below.
• Provide the model path to resume training
• Run the code

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

• Provide the test path in respective codes as sepecified below
• Download the pre-trained models from https://drive.google.com/drive/folders/1bzh9Pvr7L-NyQ2Mk-TBSlSq4TkMn2anB?usp=sharing
• Provide the model path in the code
• Run the code

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{asnani2021reverse,
      title={Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images}, 
      author={Vishal Asnani and Xi Yin and Tal Hassner and Xiaoming Liu},
      year={2021},
      eprint={2106.07873},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}