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:
@inproceedings{ge21c_interspeech,
author={Wanying Ge and Michele Panariello and Jose Patino and Massimiliano Todisco and Nicholas Evans},
title={{Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={4319--4323},
doi={10.21437/Interspeech.2021-1187}
}
This work is supported by the ExTENSoR project funded by the French Agence Nationale de la Recherche (ANR).
Codes are based on the implementations of AutoSpeech, PC-DARTS and project-NN-Pytorch-scripts.