Lip Reading-based User Authentication through Acoustic Sensing on Smartphones
Published in IEEE/ACM Transactions on Networking, 2019
Recommended citation: Li Lu, Jiadi Yu, Yingying Chen, Hongbo Liu, Yanmin Zhu, Linghe Kong, Minglu Li. "Lip Reading-based User Authentication through Acoustic Sensing on Smartphones." IEEE/ACM Transactions on Networking. 27(1). pp. 447-460. 2019. doi: 10.1109/TNET.2019.2891733.
This work was reported by IEEE Spectrum on Feburary, 2019.
Following this news, many media reprint the news to report our work. Parts of them are listed as follows.
与非网，“声纹识别技术的机会和挑战”
EEToday电子头条，“上海交大团队开发出动动嘴唇完成手机验证新平台”
360doc个人图书馆，“交大研究新的手机身份验证方式：读唇法LipPass”
BiometricUpdate.com，“Two behavioral biometric lip-reading techniques emerge”
TechTheLead，“LipPass Can Unlock Your Phone By Tracking Your Lips”
The Block，“LipPass will read your lips to keep your phone and wallet secure”
BitcoinExchangeGuide，“Lip Reading User ID Authentication System LipPass to Help Secure Phone and Wallet”
BoingBoing，“Your locked phone could verify it’s you by listening to your lips move”
IEEE/ACM Transactions on Networking is a premier journal on computer networking and communications, which is co-sponsored by IEEE ComSoc, IEEE CS and ACM with SIGCOMM. IEEE/ACM ToN is a CCF-A journal.
Abstract: To prevent users’ privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, and so on, to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although the state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Toward this end, we explore liveness verification of user authentication leveraging users’ mouth movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users’ speaking mouths through acoustic sensing on smartphones for user authentication.