标题： DFADD：基于扩散和流匹配的音频深度伪造数据集 显示英文标题

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

摘要： 主流的零样本文本转语音生产系统，如Voicebox和Seed-TTS，分别通过利用流匹配和扩散模型实现了与人类相当的语音。 不幸的是，达到人类水平的音频合成导致了身份滥用和信息安全问题。 目前，已经开发了许多针对深度伪造音频的反欺骗模型。 然而，当前最先进的反欺骗模型在对抗基于扩散和流匹配的文本转语音系统生成的音频方面的有效性仍未知。 在本文中，我们提出了基于扩散和流匹配的音频深度伪造（DFADD）数据集。 DFADD数据集收集了基于先进扩散和流匹配文本转语音模型的深度伪造音频。 此外，我们揭示了当前的反欺骗模型在对抗由扩散和流匹配文本转语音系统生成的高度类人音频方面缺乏足够的鲁棒性。 提出的DFADD数据集弥补了这一空白，并为开发更稳健的反欺骗模型提供了有价值的资源。 显示英文摘要

摘要： 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.