欺骗攻击
计算机科学
对策
软件
计算机安全
语音识别
操作系统
工程类
航空航天工程
作者
Hemlata Tak,José Patino,Massimiliano Todisco,Andreas Nautsch,Nicholas Evans,Anthony Larcher
标识
DOI:10.1109/icassp39728.2021.9414234
摘要
Spoofing countermeasures aim to protect automatic speaker verification systems from being manipulated by spoofed speech signals. While results from the most recent ASVspoof 2019 evaluation show great potential to detect most forms of attack, some continue to evade detection. This paper reports the first application of RawNet2 to anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made to the original RawNet2 architecture so that it can be applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the second-best reported, while the fusion of RawNet2 and baseline countermeasures gives the second-best results reported for the full ASVspoof 2019 logical access condition. Our results are reproducible with open source software.
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