Audio deepfake detection (ADD) is essential for preventing the misuse of
synthetic voices that may infringe on personal rights and privacy. Recent
zero-shot text-to-speech (TTS) models pose higher risks as they can clone
voices with a single utterance. However, the existing ADD datasets are
outdated, leading to suboptimal generalization of detection models. In this
paper, we construct a new cross-domain ADD dataset comprising over 300 hours of
speech data that is generated by five advanced zero-shot TTS models. To
simulate real-world scenarios, we employ diverse attack methods and audio
prompts from different datasets. Experiments show that, through novel
attack-augmented training, the Wav2Vec2-large and Whisper-medium models achieve
equal error rates of 4.1\% and 6.5\% respectively. Additionally, we demonstrate
our models' outstanding few-shot ADD ability by fine-tuning with just one
minute of target-domain data. Nonetheless, neural codec compressors greatly
affect the detection accuracy, necessitating further research.

通过使用五种先进的零样本文本转语音模型生成超过 300 小时的语音数据，构建了一个新的跨领域的 ADD 数据集。通过新颖的攻击增强训练方法，Wav2Vec2-large 模型和 Whisper-medium 模型分别获得了 4.1％和 6.5％的等误差率，展示了出色的少样本 ADD 能力。然而，神经编解码器压缩技术对检测准确性产生重大影响，需要进一步研究。