This repository contains implementation of our paper Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection accepted to INTERSPEECH 2021.

pip install -r requirements.txt

The ASVspoof2019 database can be downloaded from here

The extracted data should be orginased as:

• LA/
  • ASVspoof2019_LA_dev/flac/...
  • ASVspoof2019_LA_eval/flac/...
  • ASVspoof2019_LA_train/flac/...
  • ASVspoof2019.LA.cm.dev.trl.txt
  • ASVspoof2019.LA.cm.eval.trl.txt
  • ASVspoof2019.LA.cm.train.trn.txt
  • ASVspoof2019.LA.cm.train.trn_h.txt (uploaded in /split_protocols)
  • ASVspoof2019.LA.cm.train.trn_t.txt (uploaded in /split_protocols)
  • ...

For convience, you can change the codes' default --data argument to '/path/to/your/LA', instead of typing it for each run.

To search with 4 layers with 16 initial channels, and with masked LFCC feature:

python train_search.py --layers=4 --init_channels=16 --frontend=lfcc --mask

To train with the reported best architecture in the paper, using 4 layers, 16 initial channels and masked LFCC feature:

python train_model.py --arch=ARCH --layers=4 --init_channels=16 --frontend=lfcc --mask

replace ARCH with "Genotype(normal=[('sep_conv_5x5', 0), ('sep_conv_5x5', 1), ('sep_conv_3x3', 2), ('dil_conv_5x5', 0), ('avg_pool_3x3', 2), ('max_pool_3x3', 3), ('avg_pool_3x3', 2), ('avg_pool_3x3', 4)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('dil_conv_3x3', 2), ('avg_pool_3x3', 0), ('dil_conv_5x5', 2), ('sep_conv_3x3', 3), ('dil_conv_3x3', 2), ('avg_pool_3x3', 4)], reduce_concat=range(2, 6))"

To evaluate the saved model using the same architecture in train from scratch on LA Evaluation partition:

python evaluate.py --arch=ARCH --model=/path/to/your/saved/models/epoch_x.pth --layers=4 --init_channels=16 --frontend=lfcc

also replace ARCH with the corresponding architecture.

If you use this repository, please consider citing:

(to appear)

@inproceedings{ge2021,
  author= {Ge, Wanying and Panariello, Michele and Patino, Jose and Todisco, Massimiliano and Evans, Nicholas},
  title= {{Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection}},
  booktitle={{Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association}},
  year= {2021},
  month= {August},
  address = {Brno, Czech Republic}
}

Codes are based on the implementations of AutoSpeech, PC-DARTS and project-NN-Pytorch-scripts.