MotoPrint: Reconfigurable Vibration Motor Fingerprint via Homologous Signals Learning

Device fingerprints can satisfy the high-security requirement of modern mobile applications (e.g., mobile payments) by guaranteeing the operation is performed on a trusted device. However, existing works on device fingerprints are weak to leakage, which leads to an irreversible failure of the device fingerprint authentication system after suffering from fingerprint theft attacks. The vulnerability drives us to propose a reconfigurable device fingerprint, i.e., MotoPrint, that can recover the system after suffering from such attacks. MotoPrint stems from the motor vibration that can represent in both signals of the accelerometer and the gyroscope (i.e., they are homologous motion signals). Therefore, we designed a two-path feature extracting network and a sensor-independent training strategy to eliminate sensor noise that can decline authentication performance. In addition, MotoPrint has a complete reconfiguration mechanism to cope with fingerprint leakage, which brings the damaged authentication system back to health. The evaluation of 80 stand-Alone vibration motors and 20 in-built ones shows that MotoPrint can achieve high authentication accuracy of 98.5%. Meanwhile, we also demonstrate the reconfigured MotoPrint, which can also effectively indicate the device's uniqueness with over 98% accuracy, is independent of MotoPrints under other stimulating codes.