Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current audio deepfake models fall short of this desideratum. In this work we investigate the potential of pretrained self-supervised representations in building general and calibrated audio deepfake detection models. We show that large frozen representations coupled with a simple logistic regression classifier are extremely effective in achieving strong generalisation capabilities: compared to the RawNet2 model, this approach reduces the equal error rate from 30.9% to 8.8% on a benchmark of eight deepfake datasets, while learning less than 2k parameters. Moreover, the proposed method produces considerably more reliable predictions compared to previous approaches making it more suitable for realistic use.

翻译：泛化性——即模型在未见数据上表现良好的能力——对于构建可靠的深度伪造检测器至关重要。然而，近期研究表明，当前的音频深度伪造模型尚未达到这一要求。本工作探讨了预训练自监督表示在构建泛化且可校准的音频深度伪造检测模型中的潜力。我们发现，大型冻结表示配合简单的逻辑回归分类器能极为有效地实现强大的泛化能力：相较于RawNet2模型，该方法在包含八个深度伪造数据集的基准测试上将等错误率从30.9%降至8.8%，同时学习的参数不足2k。此外，所提方法相比先前方案能产生显著更可靠的预测，因而更适用于实际应用场景。