State Identification of Smart Devices Using Acoustic Fingerprinting

Abstract

The widespread use of smart phones has brought the security and privacy problems. In the past research, fingerprinting of physical device can be used in authenticating system. However, using the fingerprinting to identify and track the user is useful in many legitimate scenes. In this paper, we propose a novel acoustic fingerprinting approach that uses the microphones and speakers of devices to assess the state of target device. While a person is walking or running with a smart phone, a certain amount of vibration is generated, which can be investigated as the basis of state identification. For mining the subtle vibration variations, firstly, a special high-frequency control simulation to speakers is introduced. Then an unsupervised learning method called Incremental Slow Feature Analysis (IncSFA) is applied to construct the state fingerprinting to represent the slow variation triggered by external driving. At last, two alternate approaches as support vector machine (SVM) and Bayes classifier are used to classify the different states from different smart phones.