  The cues needed to detect spoofing attacks against automatic speaker
verification are often located in specific spectral sub-bands or temporal
segments. Previous works show the potential to learn these using either
spectral or temporal self-attention mechanisms but not the relationships
between neighbouring sub-bands or segments. This paper reports our use of graph
attention networks (GATs) to model these relationships and to improve spoofing
detection performance. GATs leverage a self-attention mechanism over graph
structured data to model the data manifold and the relationships between nodes.
Our graph is constructed from representations produced by a ResNet. Nodes in
the graph represent information either in specific sub-bands or temporal
segments. Experiments performed on the ASVspoof 2019 logical access database
show that our GAT-based model with temporal attention outperforms all of our
baseline single systems. Furthermore, GAT-based systems are complementary to a
set of existing systems. The fusion of GAT-based models with more conventional
countermeasures delivers a 47% relative improvement in performance compared to
the best performing single GAT system.
