The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio

ALM-based deepfake audio

This is the official repo of our work titled The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio, which was available on arxiv at https://arxiv.org/abs/2405.04880.

📚 Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:

Codecfake dataset	Description	Link
training set (part 1 of 3) & label	train_split.zip & train_split.z01 - train_split.z06	https://zenodo.org/records/11171708
training set (part 2 of 3)	train_split.z07 - train_split.z14	https://zenodo.org/records/11171720
training set (part 3 of 3)	train_split.z15 - train_split.z19	https://zenodo.org/records/11171724
development set	dev_split.zip & dev_split.z01 - dev_split.z02	https://zenodo.org/records/11169872
test set (part 1 of 2)	Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip	https://zenodo.org/records/11169781
test set (part 2 of 2)	Codec unseen test: C7.zip	https://zenodo.org/records/11125029

🛡️ Countermeasure

1. Data prepraring

Upon downloading the Codecfake datasets, please arrange them in accordance with the directory structure outlined below:

├── Codecfake
│   ├── label
│   │   └── *.txt
│   ├── train
│   │   └── *.wav (740,747 samples)
│   ├── dev
│   │   └── *.wav (92,596 samples)
│   ├── test
│   │   └── C1
│   │   │   └── *.wav (26,456 samples)
│   │   └── C2
│   │   │   └── *.wav (26,456 samples)
│   │   └── C3
│   │   │   └── *.wav (26,456 samples)
│   │   └── C4
│   │   │   └── *.wav (26,456 samples)
│   │   └── C5
│   │   │   └── *.wav (26,456 samples)
│   │   └── C6
│   │   │   └── *.wav (26,456 samples)
│   │   └── C7
│   │   │   └── *.wav (145,505 samples)
│   │   └── A1
│   │   │   └── *.wav (8,902 samples）
│   │   └── A2
│   │   │   └── *.wav (8,902 samples）
│   │   └── A3
│   │   │   └── *.wav (99,112 samples）

If you want to co-training with ASVspoof2019, Please download the training, development and evaluation set from ASVspoof2019 LA Database first.

2. Offline Feature Extraction

python preprocess.py

Please ensure the data and label position are correct. If you need to adjust, please modify in ./preprocess.py.

After preprocess, the hidden states of wav2vec2 will be saved in /data2/xyk/codecfake/preprocess_xls-r-5.

3. Train

Training on different task:

python main_train.py -t 19LA 
python main_train.py -t codecfake
python main_train.py -t co-train

Before running the main_train.py, please change the path_to_features according to the location of pre-processed featrue on your machine.

If training is slow, consider adjusting the num_worker parameter in conjunction with the number of CPU cores. The default is set to 8. If performance remains slow, you may explore multi-GPU training in args.

4. Test

Testing on different datasets was performed using the best pre-trained model, which is saved in ./models/try/anti-spoofing_feat_model.pt:

python generate_score.py -t 19LA
python generate_score.py -t ITW
python generate_score.py -t codecfake

The result will be saved in ./result.

python evaluate_score.py

You will get the final test EER.

5. Pre-trained model

We provide pre-trained models and score results as mentioned in our paper, you can use our pre-trained models testing on other condition. The inference step can refer section 4.

Vocoder-trained ADD models:

./pretrained_model/vocoder_mellcnn/anti-spoofing_feat_model.pt
./pretrained_model/vocoder_w2v2lcnn/anti-spoofing_feat_model.pt
./pretrained_model/vocoder_w2v2aasist/anti-spoofing_feat_model.pt

Codec-trained ADD models:

./pretrained_model/codec_mellcnn/anti-spoofing_feat_model.pt
./pretrained_model/codec_w2v2lcnn/anti-spoofing_feat_model.pt
./pretrained_model/codec_w2v2aasist/anti-spoofing_feat_model.pt

Co-trained ADD model:

./pretrained_model/cotrain_w2v2aasist/anti-spoofing_feat_model.pt

📝 Citation

If you find our dataset or countermeasure is useful to your research, please cite it as follows:

@article{xie2024codecfake,
  title={The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio},
  author={Xie, Yuankun and Lu, Yi and Fu, Ruibo and Wen, Zhengqi and Wang, Zhiyong and Tao, Jianhua and Qi, Xin and Wang, Xiaopeng and Liu, Yukun and Cheng, Haonan and others},
  journal={arXiv preprint arXiv:2405.04880},
  year={2024}
}

xacheng1996 / codecfake Goto Github PK

codecfake's Introduction

The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio

ALM-based deepfake audio

📚 Codecfake Dataset

🛡️ Countermeasure

1. Data prepraring

2. Offline Feature Extraction

3. Train

4. Test

5. Pre-trained model

Vocoder-trained ADD models:

Codec-trained ADD models:

Co-trained ADD model:

📝 Citation

codecfake's People

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