Exploring Diverse Feature Extractions for Adversarial Audio Detection

Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks. In the audio domain, malicious audio examples generated by adversarial attacks can cause significant performance degradation and system malfunctions, resulting in security and safety concerns. However, compared to recent developments in the audio domain, the properties of the adversarial audio examples and defenses against them still remain largely unexplored. In this study, to provide a deeper understanding of the adversarial robustness in the audio domain, we first investigate traditional and recent feature extractions in terms of adversarial attacks. We show that adversarial audio examples generated from different feature extractions exhibit different noise patterns, and thus can be distinguished by a simple classifier. Based on the observation, we extend existing adversarial detection methods by proposing a new detection method that detects adversarial audio examples using an ensemble of diverse feature extractions. By combining the frequency and self-supervised feature representations, the proposed method provides a high detection rate against both white-box and black-box adversarial attacks. Our empirical results demonstrate the effectiveness of the proposed method in speech command classification and speaker recognition.