MandiPass: Secure and Usable User Authentication via Earphone IMU
Jianwei Liu, Wenfan Song, Leming Shen, Jinsong Han, Xian Xu, Kui Ren
Abstract
Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods and inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely MandiPass. MandiPass leverages inertial measurement units (IMU), which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user's mandible. The authentication merely requires user to voice a short ‘EMM’ for generating the vibration. In this way, MandiPass enables a secure and user-friendly biometric-based authentication. We theoretically validate the feasibility of MandiPass and develop a two-branch deep neural network for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that MandiPass can achieve an equal error rate of 1.28%, even under various harsh environments.