Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection

Beijing Jiaotong University, YanShan University, A*Star

Reference github repository for the paper Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection.

@misc{tan2023rethinking,
      title={Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection}, 
      author={Chuangchuang Tan and Huan Liu and Yao Zhao and Shikui Wei and Guanghua Gu and Ping Liu and Yunchao Wei},
      year={2023},
      eprint={2312.10461},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

News 🆕

• 2024/02: NPR is accepted by CVPR 2024! Congratulations and thanks to my all co-authors!
• 2024/05: 🤗Online Demo

Environment setup

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

pip install -r requirements.txt

In order to ensure the reproducibility of the results, we provide the following suggestions：

• Docker image: nvcr.io/nvidia/tensorflow:21.02-tf1-py3
• Conda environment: ./pytorch18/bin/python
• Random seed during testing period: Random seed

Getting the data

pip install gdown==4.7.1

chmod 777 ./download_dataset.sh

./download_dataset.sh

Directory structure

Training the model

CUDA_VISIBLE_DEVICES=0 ./pytorch18/bin/python train.py --name 4class-resnet-car-cat-chair-horse --dataroot ./datasets/ForenSynths_train_val --classes car,cat,chair,horse --batch_size 32 --delr_freq 10 --lr 0.0002 --niter 50

Testing the detector

Modify the dataroot in test.py.

CUDA_VISIBLE_DEVICES=0 ./pytorch18/bin/python test.py --model_path ./NPR.pth  --batch_size {BS}

Detection Results

AIGCDetectBenchmark using ProGAN-4class checkpoint

When testing on AIGCDetectBenchmark, set no_resize and no_crop to True, and set batch_size to 1. To deal with images of odd sizes, add the following code in network/resnet.py.

n,c,w,h = x.shape
if w%2 == 1 : x = x[:,:,:-1,:]
if h%2 == 1 : x = x[:,:,:,:-1]

GenImage

./pytorch18/bin/python  train.py --dataroot {GenImage Path} --name sdv4_bs32_ --batch_size 32 --lr 0.0002 --niter 1  --cropSize 224 --classes sdv4 

Train with sdv4 as the training set, using a random seed of 70. Pretrained checkpoint.

Acknowledgments

This repository borrows partially from the CNNDetection.