  Adversarial attacks can drastically degrade performance of recommenders and
other machine learning systems, resulting in an increased demand for defence
mechanisms. We present a new line of defence against attacks which exploit a
vulnerability of recommenders that operate in high dimensional data spaces (the
so-called hubness problem). We use a global data scaling method, namely Mutual
Proximity (MP), to defend a real-world music recommender which previously was
susceptible to attacks that inflated the number of times a particular song was
recommended. We find that using MP as a defence greatly increases robustness of
the recommender against a range of attacks, with success rates of attacks
around 44% (before defence) dropping to less than 6% (after defence).
Additionally, adversarial examples still able to fool the defended system do so
at the price of noticeably lower audio quality as shown by a decreased average
SNR.
