February 26, 2020

418 words 2 mins read



Code for the paper: CNN-generated images are surprisingly easy to spot… for now https://peterwang512.github.io/CNNDetection/

repo name PeterWang512/CNNDetection
repo link https://github.com/PeterWang512/CNNDetection
language Python
size (curr.) 6507 kB
stars (curr.) 343
created 2019-12-23
license Other

Detecting CNN-Generated Images [Project Page]

CNN-generated images are surprisingly easy to spot…for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros. To appear in CVPR, 2020.

(1) Setup

Install packages

  • Install PyTorch (pytorch.org)
  • pip install -r requirements.txt

Download model weights

  • Run bash weights/download_weights.sh

(2) Quick start

# Model weights need to be downloaded.
python demo.py examples/real.png weights/blur_jpg_prob0.1.pth
python demo.py examples/fake.png weights/blur_jpg_prob0.1.pth

demo.py simply runs the model on a single image, and outputs the uncalibrated prediction.

(3) Dataset

The testset evaluated in the paper can be downloaded here.

The zip file contains images from 13 CNN-based synthesis algorithms, including the 12 testsets from the paper and images downloaded from whichfaceisreal.com. Images from each algorithm are stored in a separate folder. In each category, real images are in the 0_real folder, and synthetic images are in the 1_fake folder.

Note: ProGAN, StyleGAN, StyleGAN2, CycleGAN testset contains multiple classes, which are stored in separate subdirectories.

A script for downloading the testset is as follows:

# Download the dataset
cd dataset
bash download_testset.sh
cd ..

(4) Evaluation

After the testset and the model weights are downloaded, one can evaluate the models by running:

# Run evaluation script. Model weights need to be downloaded.
python eval.py

Besides print-outs, the results will also be stored in a csv file in the results folder. Configurations such as the path of the dataset, model weight are in eval_config.py, and one can modify the evaluation by changing the configurations. The following are the models' performances on the released set:


Testset Accuracy AP
ProGAN 100.0% 100.0%
StyleGAN 87.1% 99.6%
BigGAN 70.2% 84.5%
CycleGAN 85.2% 93.5%
StarGAN 91.7% 98.2%
GauGAN 78.9% 89.5%
CRN 86.3% 98.2%
IMLE 86.2% 98.4%
SITD 90.3% 97.2%
SAN 50.5% 70.5%
Deepfake 53.5% 89.0%
StyleGAN2 84.4% 99.1%
Whichfaceisreal 83.6% 93.2%


Testset Accuracy AP
ProGAN 100.0% 100.0%
StyleGAN 73.4% 98.5%
BigGAN 59.0% 88.2%
CycleGAN 80.8% 96.8%
StarGAN 81.0% 95.4%
GauGAN 79.3% 98.1%
CRN 87.6% 98.9%
IMLE 94.1% 99.5%
SITD 78.3% 92.7%
SAN 50.0% 63.9%
Deepfake 51.1% 66.3%
StyleGAN2 68.4% 98.0%
Whichfaceisreal 63.9% 88.8%

(A) Acknowledgments

This repository borrows partially from the pytorch-CycleGAN-and-pix2pix, and the PyTorch torchvision models repositories.

(B) Citation, Contact

If you find this useful for your research, please consider citing this bibtex. Please contact Sheng-Yu Wang <sheng-yu_wang at berkeley dot edu> with any comments or feedback.

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