# Continuous Authentication for Voice Assistants

arXiv: https://arxiv.org/abs/1701.04507 · HTML: https://mlsanity.com/paper/1701.04507/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 9 of 9)

- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech](https://mlsanity.com/paper/2205.14496/) (2022)
- [GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System](https://mlsanity.com/paper/2210.01594/) (2022)
- [Towards Zero-trust Security for the Metaverse](https://mlsanity.com/paper/2302.08885/) (2023)
- [Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System](https://mlsanity.com/paper/2306.05358/) (2023)
- [Practical Speech Re-use Prevention in Voice-driven Services](https://mlsanity.com/paper/2101.04773/) (2021)
- [DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held Devices](https://mlsanity.com/paper/2107.13217/) (2021)
- [AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers](https://mlsanity.com/paper/2605.11901/) (2026)

## Abstract (arXiv)

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the ongoing trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Siri, Google Now and Cortana, have become our everyday fixtures, especially in scenarios where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. Nevertheless, the open nature of the voice channel makes voice assistants difficult to secure and exposed to various attacks as demonstrated by security researchers. In this paper, we present VAuth, the first system that provides continuous and usable authentication for voice assistants. We design VAuth to fit in various 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 that the voice assistant executes 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 an almost perfect matching accuracy with 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 different practical attacks, such as replayed attacks, mangled voice attacks, or impersonation attacks. It also has low energy and latency overheads and is compatible with most existing voice assistants.