Title: Smartphones identification through the built-in microphones with Convolutional Neural Networks
Authors: BALDINI GIANMARCOAMERINI IRENE
Citation: IEEE ACCESS vol. 7 p. 158685-158696
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2019
JRC N°: JRC117653
ISSN: 2169-3536 (online)
URI: https://ieeexplore.ieee.org/document/8888157
https://publications.jrc.ec.europa.eu/repository/handle/JRC117653
DOI: 10.1109/ACCESS.2019.2950859
Type: Articles in periodicals and books
Abstract: The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and other applications where it is required to share sensitive data. As a consequence, security functions are important in the use of smartphones, in fact many applications require the identification and authentication of the device in mobility. This is usually achieved through cryptographic systems but recent research studies have also investigated alternative or complementary authentication mechanisms which can be used to strengthen cryptographic methods with multi-factor authentication. In this paper, we investigate the identification and authentication of smartphones using the intrinsic physical properties of the built-in microphone of the mobile phone. The possibility to identify a microphone on the basis of features extracted from audio recordings is well known in literature but it is mostly used in forensics studies and usually relies on human voice recordings. On the contrary this paper proposes an identification and authentication approach for smartphones by stimulating the built-in microphone with non-voice sounds at different frequencies. An extensive data set of 32 phones was used to evaluate experimentally the proposed approach. On the basis of the proven performance of deep learning, a Convolutional Neural Network architecture was proposed both for the identification and the authentication purposes. Its performance in comparison to other machine learning algorithms is proven in presence of different types of noises (e.g., gaussian white noise, babble noise and street noise). Satisfactory results have been obtained showing that the exploitation of a fingerprint from the microphone sensor is a good choice to assess smartphone distinctiveness.
JRC Directorate:Space, Security and Migration

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