论文标题
使用多通道音频检测重播攻击：一种基于神经网络的方法
Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
论文作者
论文摘要
随着使用语音作为主要输入的安全敏感系统的迅速增长，解决这些系统的潜在重播攻击的脆弱性变得越来越重要。以前解决此问题的努力主要集中在单渠道音频上。在本文中，我们介绍了一种新型的基于神经网络的重播攻击检测模型，该模型进一步利用了多通道音频的空间信息，并能够显着提高重播攻击检测性能。
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.
