  The performance of spoofing countermeasure systems depends fundamentally upon
the use of sufficiently representative training data. With this usually being
limited, current solutions typically lack generalisation to attacks encountered
in the wild. Strategies to improve reliability in the face of uncontrolled,
unpredictable attacks are hence needed. We report in this paper our efforts to
use self-supervised learning in the form of a wav2vec 2.0 front-end with fine
tuning. Despite initial base representations being learned using only bona fide
data and no spoofed data, we obtain the lowest equal error rates reported in
the literature for both the ASVspoof 2021 Logical Access and Deepfake
databases. When combined with data augmentation,these results correspond to an
improvement of almost 90% relative to our baseline system.
