  Audio anti-spoofing for automatic speaker verification aims to safeguard
users' identities from spoofing attacks. Although state-of-the-art spoofing
countermeasure(CM) models perform well on specific datasets, they lack
generalization when evaluated with different datasets. To address this
limitation, previous studies have explored large pre-trained models, which
require significant resources and time. We aim to develop a compact but
well-generalizing CM model that can compete with large pre-trained models. Our
approach involves multi-dataset co-training and sharpness-aware minimization,
which has not been investigated in this domain. Extensive experiments reveal
that proposed method yield competitive results across various datasets while
utilizing 4,000 times less parameters than the large pre-trained models.
