Small adversarial perturbations of input data are able to drastically change performance of machine learning systems, thereby challenging the validity of such systems. We present the very first end-to-end adversarial attacks on a music instrument classification system allowing to add perturbations directly to audio waveforms instead of spectrograms. Our attacks are able to reduce the accuracy close to a random baseline while at the same time keeping perturbations almost imperceptible and producing misclassifications to any desired instrument.
@article{arxiv.2007.14714,
title = {End-to-End Adversarial White Box Attacks on Music Instrument Classification},
author = {Katharina Prinz and Arthur Flexer},
journal= {arXiv preprint arXiv:2007.14714},
year = {2020}
}