CodecFake Source Tracing

The complete codebase is coming soon!

🛠️ Setup

Dataset Download

Download the CodecFake+ dataset (The dataset is coming soon !)

CodecFake+/
├── all_data_16k/          # CoRS + maskgct_vctk set
│   ├── p225_001_audiodec_24k_320d.wav
│   ├── p225_001_bigcodec.wav
│   ├── ....
│   └── s5_400_xocdec_hubert_general_audio.wav
└── SLMdemos_16k/          # CoSG set
    ├── SIMPLESPEECH1/     
    ├── VIOLA/
    ├── ....
    └── MASKGCT/

Pretrained Weights Setup

• For Wav2Vec2-AASIST

  • Place xlsr2_300m.pt directly into w2v2_aasist_baseline/

• For SAST Net

  • Create directory Pretrain_weight inside SAST_Net/
  • Download and place the following checkpoints in SAST_Net/Pretrain_weight:

  Model	Description	Download
  xlsr2_300m.pt	Wav2Vec2 pretrained weight	Download
  mae_pretrained_base.pth	AudioMAE pretrained on AudioSet	Download
  tuned_weight.pth	Wav2Vec2-AASIST on CodecFake+	Download

Environment Setup

conda env create -f environment.yml
conda activate CodecFakeSourceTracing

---

🚀 Inference

Notation

• Tasks

  • BIN: Binary spoof detection task
  • VQ: Vector quantization source tracing task
  • AUX: Auxiliary training objective source tracing task
  • DEC: Decoder type source tracing task

• Training Subsets

  • vq: VQ taxonomy sampling (MVQ : SVQ : SQ = 1:1:1)
  • aux: AUX taxonomy sampling (None : Semantic Distillation : Disentanglement = 1:1:1)
  • dec: DEC taxonomy sampling (Time : Freqency = 1:1)

Model Checkpoints

• Wav2Vec2-AASIST

  Single-Task Learning Models

  Model	Task	Trained Dataset	Download Links
  S_BIN	BIN	vq / aux / dec	vq • aux • dec
  S_VQ	VQ	vq	Download
  S_AUX	AUX	aux	Download
  S_DEC	DEC	dec	Download

  Dual-Task Learning Models

  Model	Task	Trained Dataset	Download Links
  D_VQ	BIN / VQ	vq	Download
  D_AUX	BIN / AUX	aux	Download
  D_DEC	BIN / DEC	dec	Download

  Multi-Task Learning Models

  Model	Task	Trained Dataset	Download Links
  M1	BIN / VQ / AUX / DEC	vq / aux / dec	vq • aux • dec
  M2	VQ / AUX / DEC	vq / aux / dec	vq • aux • dec

• SAST Net

  Model	Task	Trained Dataset	Download Links
  SAST Net	BIN	vq / aux / dec	vq • aux • dec
  	VQ	vq	Download
  	AUX	aux	Download
  	DEC	dec	Download

Running Inference

• Wav2Vec2-AASIST

  cd w2v2_aasist_baseline/
  bash inference.sh ${dataset_type} ${base_dir} ${checkpoint_path} ${model_type}

  Parameters:

  • dataset_type: "CoRS" or "CoSG"
  • base_dir: Path to dataset directory
    • For CoRS: "CodecFake+/all_data_16k/"
    • For CoSG: "CodecFake+/SLMdemos_16k/"
  • checkpoint_path: Path to model checkpoint
  • model_type: S_BIN / S_VQ / S_AUX / S_DEC / D_VQ / D_AUX / D_DEC / M1 / M2

• SAST Net

  cd SAST_Net/
  bash inference.sh ${base_dir} ${dataset_type} ${checkpoint_path} ${task} ${eval_output}

  Parameters:

  • base_dir: Path to dataset directory
  • dataset_type: "CoRS" or "CoSG"
  • checkpoint_path: Path to model checkpoint
  • task: Bin / AUX / DEC / VQ
  • eval_output: Results directory (default: "./Result")

---

🎯 Training

• Wav2Vec2-AASIST

  cd w2v2_aasist_baseline/
  bash train.sh ${base_dir} ${batch_size} ${num_epochs} ${lr} ${model_type} ${sampling_strategy}

  Parameters:

  • base_dir: Path to "CodecFake+/all_data_16k/"
  • batch_size: Batch size (default: 8)
  • num_epochs: Training epochs (default: 20)
  • lr: Learning rate (default: 1e-06)
  • model_type: S_BIN / S_VQ / S_AUX / S_DEC / D_VQ / D_AUX / D_DEC / M1 / M2
  • sampling_strategy: VQ / AUX / DEC

• SAST Net

  cd SAST_Net
  bash train.sh ${base_dir} ${save_dir} ${batch_size} ${num_epochs} ${lr} ${task} ${sampling_strategy} ${mask_ratio}

  Parameters:

  • base_dir: Path to "CodecFake+/all_data_16k/"
  • save_dir: Checkpoint save directory (default: ./models_SAST_Net)
  • batch_size: Batch size (default: 12)
  • num_epochs: Training epochs (default: 40)
  • lr: Learning rate (default: 5e-06)
  • task: Bin / VQ / AUX / DEC
  • sampling_strategy: VQ / AUX / DEC
  • mask_ratio: MAE mask ratio (default: 0.4)

📚 Citation

If this work helps your research, please consider citing our papers:

@article{chen2025codec,
  title={Codec-Based Deepfake Source Tracing via Neural Audio Codec Taxonomy},
  author={Chen, Xuanjun and Lin, I-Ming and Zhang, Lin and Du, Jiawei and Wu, Haibin and Lee, Hung-yi and Jang, Jyh-Shing Roger Jang},
  journal={arXiv preprint arXiv:2505.12994},
  year={2025}
}

@article{chen2025towards,
  title={Towards Generalized Source Tracing for Codec-Based Deepfake Speech},
  author={Chen, Xuanjun and Lin, I-Ming and Zhang, Lin and Wu, Haibin and Lee, Hung-yi and Jang, Jyh-Shing Roger Jang},
  journal={arXiv preprint arXiv:2506.07294},
  year={2025}
}

@article{chen2025codecfake+,
  title={CodecFake+: A Large-Scale Neural Audio Codec-Based Deepfake Speech Dataset},
  author={Chen, Xuanjun and Du, Jiawei and Wu, Haibin and Zhang, Lin and Lin, I and Chiu, I and Ren, Wenze and Tseng, Yuan and Tsao, Yu and Jang, Jyh-Shing Roger and others},
  journal={arXiv preprint arXiv:2501.08238},
  year={2025}
}