Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
arxiv(2021)
摘要
Substantial progress in spoofing and deepfake detection has been made in
recent years. Nonetheless, the community has yet to make notable inroads in
providing an explanation for how a classifier produces its output. The
dominance of black box spoofing detection solutions is at further odds with the
drive toward trustworthy, explainable artificial intelligence. This paper
describes our use of SHapley Additive exPlanations (SHAP) to gain new insights
in spoofing detection. We demonstrate use of the tool in revealing unexpected
classifier behaviour, the artefacts that contribute most to classifier outputs
and differences in the behaviour of competing spoofing detection models. The
tool is both efficient and flexible, being readily applicable to a host of
different architecture models in addition to related, different applications.
All results reported in the paper are reproducible using open-source software.
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