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 wake-word detection system



Adversarial Music: Real world Audio Adversary against Wake-word Detection System

Neural Information Processing Systems

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. Our goal is to jam 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.


Adversarial Music: Real world Audio Adversary against Wake-word Detection System

Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze

Neural Information Processing Systems

V oice Assistants (V As) 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 V As while our audio adversary is present. We implemented an emulated wake-word detection system of Amazon Alexa based on recent publications.


Adversarial Music: Real world Audio Adversary against Wake-word Detection System

Neural Information Processing Systems

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. Our goal is to jam 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. Then we computed our audio adversaries with consideration of expectation over transform and we implemented our audio adversary with a differentiable synthesizer.


Adversarial Music: Real world Audio Adversary against Wake-word Detection System

Li, Juncheng, Qu, Shuhui, Li, Xinjian, Szurley, Joseph, Kolter, J. Zico, Metze, Florian

Neural Information Processing Systems

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. Our goal is to jam 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. Then we computed our audio adversaries with consideration of expectation over transform and we implemented our audio adversary with a differentiable synthesizer.