Despite several years of research in deepfake and spoofing
detection for automatic speaker verification, little is known
about the artefacts that classifiers use to distinguish between
bona fide and spoofed utterances. An understanding of these is
crucial to the design of trustworthy, explainable solutions. In
this paper we report an extension of our previous work to better
understand classifier behaviour to the use of SHapley Additive
exPlanations (SHAP) to attack analysis. Our goal is to identify
the artefacts that characterise utterances generated by different
attacks algorithms. Using a pair of classifiers which operate
either upon raw waveforms or magnitude spectrograms, we
show that visualisations of SHAP results can be used to identify
attack-specific artefacts and the differences and consistencies
between synthetic speech and converted voice spoofing attacks.
