TY - GEN
T1 - WiVo
T2 - 19th ACM International Symposium on Mobile Ad-Hoc Networking and Computing, MobiHoc 2018
AU - Meng, Yan
AU - Wang, Zichang
AU - Zhang, Wei
AU - Wu, Peilin
AU - Zhu, Haojin
AU - Liang, Xiaohui
AU - Liu, Yao
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - With the prevalent of smart devices and home automations, voice command has become a popular User Interface (UI) channel in the IoT environment. Although Voice Control System (VCS) has the advantages of great convenience, it is extremely vulnerable to the spoofing attack (e.g., replay attack, hidden/inaudible command attack) due to its broadcast nature. In this study, we present WiVo, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without any additional devices or sensors carried by the users. The basic motivation of WiVo is to distinguish the authentic voice command from a spoofed one via its corresponding mouth motions, which can be captured and recognized by wireless signals. To achieve this goal, WiVo builds a theoretical model to characterize the correlation between wireless signal dynamics and the user's voice syllables. WiVo extracts the unique features from both voice and wireless signals, and then calculates the consistency between these different types of signals in order to determine whether the voice command is generated by the authentic user of VCS or an adversary. To evaluate the effectiveness of WiVo, we build a testbed based on Samsung SmartThings framework and include WiVo as a new application, which is expected to significantly enhance the security of the existing VCS. We have evaluated WiVo with 6 participants and different voice commands. Experimental evaluation results demonstrate that WiVo achieves the overall 99% detection rate with 1% false accept rate and has a low latency.
AB - With the prevalent of smart devices and home automations, voice command has become a popular User Interface (UI) channel in the IoT environment. Although Voice Control System (VCS) has the advantages of great convenience, it is extremely vulnerable to the spoofing attack (e.g., replay attack, hidden/inaudible command attack) due to its broadcast nature. In this study, we present WiVo, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without any additional devices or sensors carried by the users. The basic motivation of WiVo is to distinguish the authentic voice command from a spoofed one via its corresponding mouth motions, which can be captured and recognized by wireless signals. To achieve this goal, WiVo builds a theoretical model to characterize the correlation between wireless signal dynamics and the user's voice syllables. WiVo extracts the unique features from both voice and wireless signals, and then calculates the consistency between these different types of signals in order to determine whether the voice command is generated by the authentic user of VCS or an adversary. To evaluate the effectiveness of WiVo, we build a testbed based on Samsung SmartThings framework and include WiVo as a new application, which is expected to significantly enhance the security of the existing VCS. We have evaluated WiVo with 6 participants and different voice commands. Experimental evaluation results demonstrate that WiVo achieves the overall 99% detection rate with 1% false accept rate and has a low latency.
KW - Liveness Detection
KW - Voice Control System
KW - Wireless Side Channels
UR - https://www.scopus.com/pages/publications/85049843908
UR - https://www.scopus.com/pages/publications/85049843908#tab=citedBy
U2 - 10.1145/3209582.3209591
DO - 10.1145/3209582.3209591
M3 - Conference contribution
AN - SCOPUS:85049843908
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 81
EP - 90
BT - Mobihoc 2018 - Proceedings of the 2018 19th International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
Y2 - 26 June 2018 through 29 June 2018
ER -