基於神經網路的多通道音頻重放攻擊方法（CS SD）

• 2020 年 3 月 27 日
• 筆記

隨著使用語音作為主要輸入的安全敏感系統數量的快速增長，解決這些系統對重放攻擊的潛在脆弱性變得越來越重要。以前解決這個問題的努力主要集中在單通道音頻上。本文提出了一種新的基於神經網路的重放攻擊檢測模型，該模型進一步利用了多通道音頻的空間資訊，能夠顯著提高重放攻擊的檢測性能。

原文題目：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