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arXiv · 2409.08731

DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset

Jiawei Du·I-Ming Lin·I-Hsiang Chiu·Xuanjun Chen·Haibin Wu·Wenze Ren·Yu Tsao·Hung-yi Lee·Jyh-Shing Roger Jang
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Abstract

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.

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Jiawei Du, I-Ming Lin, I-Hsiang Chiu, Xuanjun Chen, Haibin Wu, Wenze Ren, Yu Tsao, Hung-yi Lee, Jyh-Shing Roger Jang. 2024-09-13. DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset. https://arxiv.org/abs/2409.08731

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