Cross-Domain Audio Deepfake Detection: Dataset and Analysis
Yuang Li, Min Zhang, Mengxin Ren, Xiaosong Qiao, Miaomiao Ma, Daimeng Wei, Hao Yang · 2024
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(TTS) models pose higher risks as they can clone voices with a single utterance.However, the existing ADD datasets are outdated, leading to suboptimal generalization of detection models.In this paper, we construct a new cross-domain ADD dataset comprising over 300 hours of speech data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 zeroshot TTS models.To simulate real-world scenarios, we employ diverse attack methods and audio prompts from different datasets.Experiments show that, through novel attackaugmented 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.Our dataset is publicly available 1 .