论文标题
检测具有辍学不确定性的ASR系统的音频攻击
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty
论文作者
论文摘要
最近已经开发了各种对抗性音频攻击，以欺骗自动语音识别（ASR）系统。我们在这里提出了基于神经网络中辍学引入的不确定性的辩护。我们表明，我们的防御能够通过在最新的端到端ASR系统上进行优化的扰动和频率掩盖来检测攻击。此外，防守可以强大地抵抗免疫降噪的攻击。我们测试了Mozilla的CommonVoice数据集，URBANSOUND数据集和LibrisPeech数据集的摘录，这表明它在广泛的方案中实现了很高的检测准确性。
Various adversarial audio attacks have recently been developed to fool automatic speech recognition (ASR) systems. We here propose a defense against such attacks based on the uncertainty introduced by dropout in neural networks. We show that our defense is able to detect attacks created through optimized perturbations and frequency masking on a state-of-the-art end-to-end ASR system. Furthermore, the defense can be made robust against attacks that are immune to noise reduction. We test our defense on Mozilla's CommonVoice dataset, the UrbanSound dataset, and an excerpt of the LibriSpeech dataset, showing that it achieves high detection accuracy in a wide range of scenarios.
