# Reverse_Engineering_GMs

**Repository Path**: a844304090/Reverse_Engineering_GMs

## Basic Information

- **Project Name**: Reverse_Engineering_GMs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-30
- **Last Updated**: 2021-06-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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


![alt text](https://github.com/vishal3477/Reverse_Engineering_GMs/blob/main/image/teaser_resized.png?raw=true)
## 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}
}
```