Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

Beijing Jiaotong University, YanShan University, A*Star

Reference github repository for the paper Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning. FreqNet is accepted by AAAI 2024!

@misc{tan2024frequencyaware,
      title={Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning}, 
      author={Chuangchuang Tan and Yao Zhao and Shikui Wei and Guanghua Gu and Ping Liu and Yunchao Wei},
      year={2024},
      eprint={2403.07240},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

TODO

• Random seed setting ensures reproducibility of results.
• [ ] Pre-Trained Model release
• [ ] Update link to paper
• [ ] Pretrained models
• [ ] Dataset download
• [ ] Code released

Environment setup

Classification environment: We recommend installing the required packages by running the command:

pip install -r requirements.txt

Getting the data

Download dataset from CNNDetection CVPR2020, GANGen-Detection (googledrive).

pip install gdown==4.7.1

chmod 777 ./download_dataset.sh

./download_dataset.sh

Training the model

CUDA_VISIBLE_DEVICES=0 python train.py --name 4class-resnet-car-cat-chair-horse --dataroot {CNNDetection-Path} --classes car,cat,chair,horse --batch_size 32 --delr_freq 10 --lr 0.001 --niter 85

GPU Server information

Our GPU Server information is as follows:(Paper results)
CPU : Hygon C86 7169 24-core Processor
GPU : A4000
Nvidia Driver : 495.29.05
Ubuntu Version: 20.04.1 LTS

Testing the detector

Modify the dataroot in test.py.

CUDA_VISIBLE_DEVICES=0 python test.py --model_path ./4-classes-freqnet.pth  --batch_size {BS}

Acknowledgments

This repository borrows partially from the CNNDetection, NPR.