Defending a Music Recommender Against Hubness-Based Adversarial Attacks

Katharina Hoedt; Arthur Flexer; Gerhard Widmer

doi:10.5281/zenodo.6573391

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arXiv · 2205.12032

Defending a Music Recommender Against Hubness-Based Adversarial Attacks

Katharina Hoedt·Arthur Flexer·Gerhard Widmer

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Abstract

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.

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Katharina Hoedt, Arthur Flexer, Gerhard Widmer. 2022-05-24. Defending a Music Recommender Against Hubness-Based Adversarial Attacks. https://doi.org/10.5281/zenodo.6573391

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