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.
Cite
@article{arxiv.2205.12032,
title = {Defending a Music Recommender Against Hubness-Based Adversarial Attacks},
author = {Katharina Hoedt and Arthur Flexer and Gerhard Widmer},
journal= {arXiv preprint arXiv:2205.12032},
year = {2022}
}
Comments
6 pages, to be published in Proceedings of the 19th Sound and Music Computing Conference 2022 (SMC-22)