扬声器方向感知隐私控制,以防止误激活语音助手

Shaohu Zhang, Aafaq Sabir, Anupam Das
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引用次数: 0

摘要

亚马逊Echo和谷歌home等智能家居语音助手(VAs)因通过语音命令提供便利而受到欢迎。VAs持续侦听唤醒命令,并将后续音频数据发送给制造商拥有的云服务进行处理,以识别可操作的命令。然而,研究表明,当唤醒词在背景中被说出时(无论是由人类还是通过机械扬声器播放),自动语音助理容易受到重放攻击和意外激活。现有的隐私控制在防止此类误激活方面并不有效。这引起了用户的隐私和安全问题,因为他们的对话可以在他们不知情的情况下被记录并转发到云。最近的研究表明,在与VA等保护剂互动时,视觉凝视起着重要作用,用户在调用VA时倾向于将他们的头或身体转向VA。在本文中,我们提出了一种称为HeadTalk的无设备,非突发性声学传感系统来阻止VAs的误激活。所提出的系统利用用户的头部方向信息,并验证人类是否产生声音,以最大限度地减少意外激活。我们的广泛评估表明,HeadTalk可以准确地推断说话者的头部方向,平均准确率为96.14%,区分人声和机械说话者的错误率为2.58%。我们还进行了一项用户交互研究,以评估与现有隐私控制相比,用户如何看待我们提出的方法。我们的研究结果表明,HeadTalk不仅可以增强虚拟网关的安全性和隐私控制,而且可以在不需要任何额外硬件的情况下以可用的方式做到这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speaker Orientation-Aware Privacy Control to Thwart Misactivation of Voice Assistants
Smart home voice assistants (VAs) such as Amazon Echo and Google Home have become popular because of the convenience they provide through voice commands. VAs continuously listen to detect the wake command and send the subsequent audio data to the manufacturer-owned cloud service for processing to identify actionable commands. However, research has shown that VAs are prone to replay attack and accidental activations when the wake words are spoken in the background (either by a human or played through a mechanical speaker). Existing privacy controls are not effective in preventing such misactivations. This raises privacy and security concerns for the users as their conversations can be recorded and relayed to the cloud without their knowledge. Recent studies have shown that the visual gaze plays an important role when interacting with conservation agents such as VAs, and users tend to turn their heads or body toward the VA when invoking it. In this paper, we propose a device-free, non-obtrusive acoustic sensing system called HeadTalk to thwart the misactivation of VAs. The proposed system leverages the user's head direction information and verifies that a human generates the sound to minimize accidental activations. Our extensive evaluation shows that HeadTalk can accurately infer a speaker's head orientation with an average accuracy of 96.14% and distinguish human voice from a mechanical speaker with an equal error rate of 2.58%. We also conduct a user interaction study to assess how users perceive our proposed approach compared to existing privacy controls. Our results suggest that HeadTalk can not only enhance the security and privacy controls for VAs but do so in a usable way without requiring any additional hardware.
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