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Implementation of F3-Net : Frequency in Face Forgery Network
Note
This is a personal implementation of F3-Net , so there are lots of difference compared to the official version. To learn more details about F3-Net, please check the paper here.
Result
Model is tested on FaceForensics++ LQ data and reports AUC.
Model
Paper
Valid(Mine)
Test(Mine)
Baseline
89.3
92.0
89.6
FAD
90.7
91.3
89.5
LFS
88.9
87.5
84.7
Both
92.8
91.0
88.6
Mix
93.3
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Obviously, there's something wrong with the implementation of LFS branch and I'm working on it now.
Usage
Hyperparameter
Hyperparameters are in train.py.
Variable name
Description
dataset_path
The path of dataset, support FF++ only.
pretrained_path
The path of pretrained Xception model.
batch_size
128 in paper.
max_epoch
how many epochs to train the model.
loss_freq
print loss after how many iterations
mode
mode of the network, see details below.
Load a pretrained Xception
Download Xception model trained on ImageNet (through this link) or use your own pretrained Xception.
Then modify the pretrained_path variable.
Use FF++ dataset
The dataset related function is designed for FaceForensics++ dataset. Check this github repo or paper for more details of the dataset.
After preprocessing, the data should be organized as following:
Use both of branches and concate before classification.
'Mix'(unavailable)
Use both of branches and MixBlock.
Note:
Mode 'Mix' is unavailable yet. If you're interested in this part, check 'class Mixblock' in models.py.
Run
Environment:
Pytorch, torchvision, numpy, sklearn, pillow are needed.
To train the model
python train.py
Reference
Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. arXiv preprint arXiv:2007.09355, 2020
