BiasHacker: Voice Command Disruption by Exploiting Speaker Biases in Automatic Speech Recognition
Payton Walker, Nathan McClaran, Zihao Zheng, Nitesh Saxena, Guofei Gu · 2022
Modern speech recognition systems that are widely deployed today still suffer from known gender and racial biases. In this work, we demonstrate the potential to exploit the existing biases in these systems to achieve a new attack goal. We consider the potential for command disruption by an attacker that can be conducted in a manner that allows for access and control of a victim's voice assistant device. We present a novel attack, BiasHacker, which crafts specialized chatter noise to exploit racial and gender biases in speech recognition systems for the purposes of command disruption. Our experimental results confirm both racial and gender bias that is still present in the speech recognition systems of two modern smart speaker devices. We also evaluated the effectiveness of three types of chatter noise (American English (AE)-Male, Nigerian-Female, Korean-Female) for disruption and demonstrate that the AE-Male chatter is consistently more successful. Comparing the average success rate of each chatter type, in scenarios where disruption was achieved, we find that when targeting the Google Home mini smart speaker, the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 112% and 121%, respectively. Also, when targeting the Amazon Echo Dot 2 the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 42% and 69%, respectively.