Detecting and Simulating Artifacts in GAN Fake Images

##Introduction To detect GAN generated images, conventional supervised machine learning algorithms require collection of a number of real and fake images from the targeted GAN model. However, the specific model used by the attacker is often unavailable.

To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models.

AutoGAN Pipeline

Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipeline. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input.

Upsampling artifacts in StarGAN image

By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.

Upsampling artifacts in AutoGAN image

This code is the training and evaluation code for our WIFS 2019 paper (arxiv).

@inproceedings{zhang2019detecting,
title={Detecting and Simulating Artifacts in GAN Fake Images},
author={Zhang, Xu and Karaman, Svebor and Chang, Shih-Fu},
booktitle={WIFS},
year={2019}
}

Installation

Make a full clone to make sure cloning all the submodules.

git clone --recursive https://github.com/ColumbiaDVMM/AutoGAN.git

My current conda environment is attached as AutoGAN.yml

Data Preparation

Download cycleGAN image data.

# From project directory
cd autogan
python datasets/prepare_data.py

Download cycleGAN model.

# From project directory
cd autogan
python scripts/prepare_model.py

Run cycleGAN to get all cycleGAN synthesized images.

# From project directory
cd autogan
python run_test.py --dataset CycleGAN

AutoGAN Extra Preparation:

Train all autoGAN models (this may take 2-3 days):

# From project directory
cd autogan
python run_training.py

To train cityscapes and maps models, change make trainB directory under datasets/cityscapes or datasets/maps with any jpg images. Remove it once training is done. Once training is done, change all the autogan models' name in ./autogan/checkpoints/{category}_auto/latest_net_G_A.pth to ./autogan/checkpoints/{category}_auto/latest_net_G.pth

Pretrained model can be downloaded from Google Drive. Unzip to ./autogan/checkpoints/

Run autoGAN to get all autoGAN synthesized images.

# From project directory
cd autogan
python run_test.py --dataset AutoGAN

Leave-One-Out Extra Preparation (Optional):

# From project directory
cd autogan/datasets
python get_fold_information.py 

Training and Test GAN Detection Classifier

Training

The easiest way to train the classifier on all 13 semantic categories (You may change the code to run with less categories) is

# From project directory
python run_training.py --dataset=CycleGAN --feature=fft --gpu-id=0

• --dataset: Training dataset, choose from CycleGAN or AutoGAN.

• --feature: choose from image and fft for image feature or spectrum feature

• --gpu-id: GPU to use. Split by comma

Code create three directories

• ./model_resnet/: directory to save model.
• ./resnet_log/: tensorboard training log.

The code indeed call GAN_Detection_Train.py. The code gives more options for training the classifier. Run the following code for more information.

# From project directory
python GAN_Detection_Train.py -h

Test

The easiest way to train the classifier on all 13 semantic categories (You may change the code to run with less categories) is

# From project directory
python run_test.py --dataset=CycleGAN --feature=fft ```
  - `--dataset`: Training dataset, choose from CycleGAN or AutoGAN.
  
  - `--feature`: choose from image and fft for image feature or spectrum feature

Code create 1 directories

• ./final_output/: directory for the test results (csv file).

The code indeed call GAN_Detection_Test.py. The code gives more options for training the classifier. Run the following code for more information.

# From project directory
python GAN_Detection_Test.py -h