论文标题
端到端的对抗白盒子对音乐仪器分类的攻击
End-to-End Adversarial White Box Attacks on Music Instrument Classification
论文作者
论文摘要
输入数据的对抗性扰动能够大大改变机器学习系统的性能，从而挑战了此类系统的有效性。我们介绍了对音乐仪器分类系统的第一次端到端对抗攻击，允许将扰动直接添加到音频波形而不是频谱图中。我们的攻击能够降低接近随机基线的准确性，同时保持扰动几乎无法察觉，并对任何所需的仪器产生错误分类。
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.
