  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.
