FraudWhistler: A Resilient, Robust and Plug-and-play Adversarial Example Detection Method for Speaker Recognition

With the in-depth integration of deep learning, state-of-theart speaker recognition systems have achieved breakthrough progress. However, the intrinsic vulnerability of deep learning to Adversarial Example (AE) attacks has brought new severe threats to real-world speaker recognition systems. In this paper, we propose FraudWhistler, a practical AE detection system, which is resilient to various AE attacks, robust in complex physical environments, and plug-and-play for deployed systems. Its basic idea is to make use of an intrinsic characteristic of AE, i.e., the instability of model prediction for AE, which is totally different from benign samples. FraudWhistler generates several audio variants for the original audio sample with some distortion techniques, obtains multiple outputs of the speaker recognition system for these audio variants, and based on that FraudWhistler extracts some statistics representing the instability of the original audio sample and further trains a one-class SVM classifier to detect adversarial example. Extensive experimental results show that FraudWhistler achieves 98.7% accuracy on AE detection outperforming SOTA works by 13%, and 84% accuracy in the worst case against an adaptive adversary.