With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems’ potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.

中文翻译：

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使用多声道音频检测重放攻击：一种基于神经网络的方法

随着使用语音作为主要输入的安全敏感系统的数量迅速增加，解决这些系统对重放攻击的潜在脆弱性变得越来越重要。以前解决这个问题的努力主要集中在单通道音频上。在本文中，我们介绍了一种新的基于神经网络的重放攻击检测模型，该模型进一步利用了多声道音频的空间信息，能够显着提高重放攻击检测性能。