Identifying Audio Adversarial Examples via Anomalous Pattern Detection

Abstract

Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99.9% similar to a benign sample. Given the wide range of applications of DNN-based audio recognition systems from automotives to virtual assistants, detecting the presence of adversarial examples is of high practical relevance. We propose a method to detect audio adversarial samples. Employing anomalous pattern detection techniques in the activation space of these models, we show that 2 of the recent and current state-of-the-art adversarial attacks on audio processing systems systematically lead to higher-than-expected activation at some subset of nodes and we can detect these with up to an AUC of 0.98 with no degradation in performance on benign samples. Furthermore, our work strengthens the study of properties of adversarial examples that hold across multiple domains.