This is an implementation of the following paper.
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion. InterSpeech'2023
Rui Liu, Jinhua Zhang, Guanglai Gao, Haizhou Li.
First, clone the repository locally, create and activate a conda environment, and install the requirements :
$ conda create --name M2S-ADD python=3.8.8
$ conda activate M2S-ADD
$ conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
$ pip install -r requirements.txt
Our experiments are done in the logical access (LA) partition of the ASVspoof 2019 dataset, which can can be downloaded from here.
To train the model run:
python main.py
To evaluate your own model on LA evaluation dataset:
python main.py --track=logical --loss=WCE --is_eval --eval --model_path='/path/to/your/best_model.pth' --eval_output='eval_CM_scores_file.txt'
If you would like to compute scores on development dataset simply run:
python main.py --track=logical --loss=WCE --eval --model_path='/path/to/your/best_model.pth' --eval_output='dev_CM_scores_file.txt'
Compute the min t-DCF and EER(%) on development dataset
python tDCF_python_v2/evaluate_tDCF_asvspoof19_eval_LA.py dev 'dev_CM_scores_file.txt'
Compute the min t-DCF and EER(%) on evaluation dataset
python tDCF_python_v2/evaluate_tDCF_asvspoof19_eval_LA.py Eval 'eval_CM_scores_file.txt'
This repository is built on RawGAT-ST-antispoofing.
Authors would like to acknowledge other repositories as well.
For any query regarding this repository, please contact:
- Rui Liu (e-mail:liurui_imu@163.com), Jinhua Zhang(e-mail: zjh_imu@163.com)
If you use M2S-ADD for anti-spoofing please use the following citations:
@inproceedings{liu23v_interspeech,
author={Rui Liu and Jinhua Zhang and Guanglai Gao and Haizhou Li},
title={{Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion}},
year=2023,
booktitle={Proc. INTERSPEECH 2023},
pages={3999--4003},
doi={10.21437/Interspeech.2023-2335}
}