Cross-Domain Audio Deepfake Detection: Dataset and Analysis
<|reference_start|>Cross-Domain Audio Deepfake Detection: Dataset and Analysis: 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 However, the existing ADD datasets are outdated, leading to suboptimal generalization of detection models.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.<|reference_end|>
@article{li2024cross-domain,
    title={Cross-Domain Audio Deepfake Detection: Dataset and Analysis},
    author={Yuang Li, Min Zhang, Mengxin Ren, Miaomiao Ma, Daimeng Wei, Hao Yang},
    journal={arXiv preprint arXiv:2404.04904},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04904},
    primaryClass={cs.SD cs.AI eess.AS}
}