DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset
<|reference_start|>DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset: Mainstream zero-shot TTS production systems like Voicebox and Seed-TTS achieve human parity speech by leveraging Flow-matching and Diffusion models, respectively. Unfortunately, human-level audio synthesis leads to identity misuse and information security issues. Currently, many antispoofing models have been developed against deepfake audio. However, the efficacy of current state-of-the-art anti-spoofing models in countering audio synthesized by diffusion and flowmatching based TTS systems remains unknown. In this paper, we proposed the Diffusion and Flow-matching based Audio Deepfake (DFADD) dataset. The DFADD dataset collected the deepfake audio based on advanced diffusion and flowmatching TTS models. Additionally, we reveal that current anti-spoofing models lack sufficient robustness against highly human-like audio generated by diffusion and flow-matching TTS systems. The proposed DFADD dataset addresses this gap and provides a valuable resource for developing more resilient anti-spoofing models.<|reference_end|>
@article{du2024dfadd:,
    title={DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset},
    author={Jiawei Du, I-Ming Lin, I-Hsiang Chiu, Xuanjun Chen, Haibin Wu, Wenze
  Ren, Yu Tsao, Hung-yi Lee, Jyh-Shing Roger Jang},
    journal={arXiv preprint arXiv:2409.08731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08731},
    primaryClass={cs.SD eess.AS}
}