DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset
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.
Cite
@article{arxiv.2409.08731,
title = {DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset},
author = {Jiawei Du and I-Ming Lin and I-Hsiang Chiu and Xuanjun Chen and Haibin Wu and Wenze Ren and Yu Tsao and Hung-yi Lee and Jyh-Shing Roger Jang},
journal= {arXiv preprint arXiv:2409.08731},
year = {2024}
}
Comments
Accepted by IEEE SLT 2024