Face-Forgery-Detection

This is the unofficial implementation of the core two-stream model from Generalizing Face Forgery Detection with High-frequency Features (CVPR 2021) in Pytorch. The original paper [here](Generalizing Face Forgery Detection With High-Frequency Features (thecvf.com))

Model

The overall network is shown as follows

Environment

• pytorch 1.7
• dlib
• cv2
• tqdm
• Linux

Structure

|-- compare.py                # compare the results of testdata labels
|-- components                # modules of the network
|   |-- __init__.py
|   |-- __pycache__
|   |-- attention.py          # attention module
|   |-- srm_conv.py           # srm_conv module
|-- dataset.py                # load the train data or val data
|-- loss                      # Loss Function
|   |-- __pycache__
|   |-- am_softmax.py         # am_softmax loss
|-- model_core.py             # the main network in this paper
|-- networks                  # baseline network
|   |-- __pycache__
|   |-- xception-b5690688.pth # pretrained baseline network
|   |-- xception.py           # baseline network
|-- test.py                   # test code
|-- train.py                  # train code

How to Use

• For the test, run the test.py and set the args

python test.py --cuda_id 0 --test_dir /home/train --pre_model /home/model

• For the train, run the train.py and set the args

python train.py --cuda_id 0 --train_label /home/label --train_dir /home/train --val_dir /home/val --save_model /home/result

• Note

The size of training images is 256x256
The label of training images is a xxx.csv file(including all images' label: filename and label)

Reference

https://github.com/592McAvoy/face-forgery-detection