Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on
wake-word detection to respond to people's commands, which could potentially be
vulnerable to audio adversarial examples. In this work, we target our attack on
the wake-word detection system, jamming the model with some inconspicuous
background music to deactivate the VAs while our audio adversary is present. We
implemented an emulated wake-word detection system of Amazon Alexa based on
recent publications. We validated our models against the real Alexa in terms of
wake-word detection accuracy. Then we computed our audio adversaries with
consideration of expectation over transform and we implemented our audio
adversary with a differentiable synthesizer. Next, we verified our audio
adversaries digitally on hundreds of samples of utterances collected from the
real world. Our experiments show that we can effectively reduce the recognition
F1 score of our emulated model from 93.4% to 11.0%. Finally, we tested our
audio adversary over the air, and verified it works effectively against Alexa,
reducing its F1 score from 92.5% to 11.0%.; We also verified that
non-adversarial music does not disable Alexa as effectively as our music at the
same sound level. To the best of our knowledge, this is the first real-world
adversarial attack against a commercial-grade VA wake-word detection system.
Our code and demo videos can be accessed at
https://www.junchengbillyli.com/AdversarialMusic

本文针对语音助手（VAs）的唤醒词检测系统进行了实际世界中的对抗攻击，使用不起眼的背景音乐干扰唤醒词检测系统的模型，在真实场景中测试了攻击对 Alexa 的影响并证明了对抗音乐的有效性。