基于神经网络的多通道音频重放攻击方法（CS SD）

• 2020 年 3 月 27 日
• 筆記

随着使用语音作为主要输入的安全敏感系统数量的快速增长，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。本文提出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通道音频的空间信息，能够显著提高重放攻击的检测性能。

原文题目：Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

原文：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.

原文作者：Yuan Gong, Jian Yang, Christian Poellabauer

原文地址：https://arxiv.org/abs/2003.08225