Continuous Authentication for Voice Assistants

By Huan Feng, Kassem Fawaz, Kang G. Shin. ACM/IEEE International Conference on Mobile Computing and Networking, 2017

298 citations recorded by the source corpus.

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the current trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Alexa, Siri, and Google Now, have become our everyday fixtures, especially when/where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. The open nature of the voice channel makes voice assistants difficult to secure, and hence exposed to various threats as demonstrated by security researchers. To defend against these threats, we present VAuth, the first system that provides continuous authentication for voice assistants. VAuth is designed to fit in widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees the voice assistant to execute only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve 97% detection accuracy and less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts various practical attacks, such as replay attacks, mangled voice attacks, or impersonation attacks. It also incurs low energy and latency overheads and is compatible with most voice assistants.

Subfields: Information Systems, Social Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b7a1f7ac1111b29a28af6e659adb11db6dbf59df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Kofi Osei-Frimpong
2. Dacheng Tao
3. Graeme McLean
4. Jing Zhang
5. Charu C. Aggarwal

In Social Psychology

1. Kofi Osei-Frimpong
2. Graeme McLean

In Signal Processing

1. Dacheng Tao
2. Jing Zhang

Citations in the corpus

• The Insecurity of Home Digital Voice Assistants - Amazon Alexa as a Case Study (2017, background): “For example, [4] However, users may be reluctant to wear this device at home all the time.”
• Hey Alexa ... examine the variables influencing the use of artificial intelligent in-home voice assistants (2019, background): “Sophisticated voice assistants can perform high priority commends utilising personal account details, make appointments, look up service information and place orders all on behalf of their user (Feng et al. 2017).”
• Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “has many applications in various AIoT domains, for example, automatic transcription system for multiperson meetings, personalized recommendation by smart voice assistants [170],”