  Few-shot classifiers excel under limited training samples, making them useful
in applications with sparsely user-provided labels. Their unique relative
prediction setup offers opportunities for novel attacks, such as targeting
support sets required to categorise unseen test samples, which are not
available in other machine learning setups. In this work, we propose a
detection strategy to identify adversarial support sets, aimed at destroying
the understanding of a few-shot classifier for a certain class. We achieve this
by introducing the concept of self-similarity of a support set and by employing
filtering of supports. Our method is attack-agnostic, and we are the first to
explore adversarial detection for support sets of few-shot classifiers to the
best of our knowledge. Our evaluation of the miniImagenet (MI) and CUB datasets
exhibits good attack detection performance despite conceptual simplicity,
showing high AUROC scores. We show that self-similarity and filtering for
adversarial detection can be paired with other filtering functions,
constituting a generalisable concept.
