Over-the-Air Adversarial Attack Detection: from Datasets to Defenses

Li Wang; Xiaoyan Lei; Haorui He; Lei Wang; Jie Shi; Zhizheng Wu

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arXiv · 2509.09296

Over-the-Air Adversarial Attack Detection: from Datasets to Defenses

Li Wang·Xiaoyan Lei·Haorui He·Lei Wang·Jie Shi·Zhizheng Wu

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Abstract

Automatic Speaker Verification (ASV) systems can be used for voice-enabled applications for identity verification. However, recent studies have exposed these systems' vulnerabilities to both over-the-line (OTL) and over-the-air (OTA) adversarial attacks. Although various detection methods have been proposed to counter these threats, they have not been thoroughly tested due to the lack of a comprehensive data set. To address this gap, we developed the AdvSV 2.0 dataset, which contains 628k samples with a total duration of 800 hours. This dataset incorporates classical adversarial attack algorithms, ASV systems, and encompasses both OTL and OTA scenarios. Furthermore, we introduce a novel adversarial attack method based on a Neural Replay Simulator (NRS), which enhances the potency of adversarial OTA attacks, thereby presenting a greater threat to ASV systems. To defend against these attacks, we propose CODA-OCC, a contrastive learning approach within the one-class classification framework. Experimental results show that CODA-OCC achieves an EER of 11.2% and an AUC of 0.95 on the AdvSV 2.0 dataset, outperforming several state-of-the-art detection methods.

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Li Wang, Xiaoyan Lei, Haorui He, Lei Wang, Jie Shi, Zhizheng Wu. 2025-09-11. Over-the-Air Adversarial Attack Detection: from Datasets to Defenses. https://arxiv.org/abs/2509.09296

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