TY - GEN
T1 - Voice liveness detection for voice assistants using ear canal pressure
AU - Shang, Jiacheng
AU - Wu, Jie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - With the success of voice recognition techniques, users can easily control any device in smart home environments by simply saying a voice command. Based on this idea, a new group of smart devices are designed and released, which are called voice assistant. However, the voice itself is not secure and can be attacked in many ways. To defend against various types of voice replay attacks, we present a new voice liveness detection system. The basic insight of our system is that mouth opening movements will change the space size in the ear canal, which further changes the air pressure in ear canals. In this paper, we propose solutions to detect mouth opening movements using the noisy air pressure data and match them with the voices to validate the liveness of the voice source. To evaluate the effectiveness of our system, we develop a prototype on Raspberry Pi and conduct comprehensive evaluations. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with an accuracy of 91.72%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with an accuracy of 97.2%.
AB - With the success of voice recognition techniques, users can easily control any device in smart home environments by simply saying a voice command. Based on this idea, a new group of smart devices are designed and released, which are called voice assistant. However, the voice itself is not secure and can be attacked in many ways. To defend against various types of voice replay attacks, we present a new voice liveness detection system. The basic insight of our system is that mouth opening movements will change the space size in the ear canal, which further changes the air pressure in ear canals. In this paper, we propose solutions to detect mouth opening movements using the noisy air pressure data and match them with the voices to validate the liveness of the voice source. To evaluate the effectiveness of our system, we develop a prototype on Raspberry Pi and conduct comprehensive evaluations. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with an accuracy of 91.72%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with an accuracy of 97.2%.
KW - Ear canal pressure
KW - Liveness detection
KW - Voice replay attack
UR - http://www.scopus.com/inward/record.url?scp=85102189976&partnerID=8YFLogxK
U2 - 10.1109/MASS50613.2020.00089
DO - 10.1109/MASS50613.2020.00089
M3 - Conference contribution
AN - SCOPUS:85102189976
T3 - Proceedings - 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
SP - 693
EP - 701
BT - Proceedings - 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
Y2 - 10 December 2020 through 13 December 2020
ER -