FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants
This addresses a security and privacy vulnerability in IoT voice assistants, though it is incremental as it builds on known issues with wake-up word detection.
The paper tackled the problem of voice assistants being inadvertently triggered by innocent-sounding fuzzy words, known as FakeWake, by generating 965 fuzzy words for 8 popular smart speakers and proposing remedies that improved resilience and overall performance.
In the area of Internet of Things (IoT) voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles. To save power and protect user privacy, voice assistants send commands to the cloud only if a small set of pre-registered wake-up words are detected. However, voice assistants are shown to be vulnerable to the FakeWake phenomena, whereby they are inadvertently triggered by innocent-sounding fuzzy words. In this paper, we present a systematic investigation of the FakeWake phenomena from three aspects. To start with, we design the first fuzzy word generator to automatically and efficiently produce fuzzy words instead of searching through a swarm of audio materials. We manage to generate 965 fuzzy words covering 8 most popular English and Chinese smart speakers. To explain the causes underlying the FakeWake phenomena, we construct an interpretable tree-based decision model, which reveals phonetic features that contribute to false acceptance of fuzzy words by wake-up word detectors. Finally, we propose remedies to mitigate the effect of FakeWake. The results show that the strengthened models are not only resilient to fuzzy words but also achieve better overall performance on original training datasets.