Voice Presentation Attack Detection through Text-Converted Voice Command Analysis
Voice Presentation Attack Detection through Text-Converted Voice Command Analysis
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DOI：
10.1145/3290605.3300828
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Il-Youp Kwak;J. Huh;S. Han;Iljoo Kim;J. Yoon
中科院分区：
文献类型：
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作者：
Il-Youp Kwak;J. Huh;S. Han;Iljoo Kim;J. Yoon
语音助手正在迅速升级，以支持高级的安全关键命令，例如解锁设备，检查电子邮件和进行支付。在本文中，我们探讨了使用用户的文本转换的语音命令话语作为分类特征，以帮助识别用户的真正的命令，并检测可疑的命令的可行性。为了保持高检测精度，我们的方法从全局训练的攻击检测模型（立即可用于新用户）开始，并逐渐切换到针对目标用户的话语模式定制的用户特定模型。为了评估准确性，我们使用了一个真实的语音助手数据集，其中包括从260万用户那里收集的大约3460万条语音命令。我们的评估结果表明，当使用最佳阈值时，该方法能够实现约3.4%的等错误率（EER），检测到95.7%的攻击。对于那些经常使用安全关键（类似攻击）命令的人，我们仍然可以实现低于5%的EER。
Voice assistants are quickly being upgraded to support advanced, security-critical commands such as unlocking devices, checking emails, and making payments. In this paper, we explore the feasibility of using users' text-converted voice command utterances as classification features to help identify users' genuine commands, and detect suspicious commands. To maintain high detection accuracy, our approach starts with a globally trained attack detection model (immediately available for new users), and gradually switches to a user-specific model tailored to the utterance patterns of a target user. To evaluate accuracy, we used a real-world voice assistant dataset consisting of about 34.6 million voice commands collected from 2.6 million users. Our evaluation results show that this approach is capable of achieving about 3.4% equal error rate (EER), detecting 95.7% of attacks when an optimal threshold value is used. As for those who frequently use security-critical (attack-like) commands, we still achieve EER below 5%.