Microphone Identification Using Convolutional Neural Networks

The ability to identify mobile phones through their built-in components has been demonstrated in the literature for various types of sensors including charge coupled devices (CCD)/complementary metal–oxide semiconductors (CMOS), accelerometers, magnetometers, and microphones. The identification is performed by exploiting small but significant differences in the electronic circuits generated during the manufacturing process. Thus, these distinctive traces become an intrinsic property of the electronic components, which can be detected and exploited as a unique fingerprints associated with the mobile phone. Such fingerprints can be used in various scenarios, especially in security and forensics related applications. In this article, the identification of mobile phones through their built-in microphone by means of convolutional neural networks (CNNs) is investigated. In this specific context, CNNs have received very limited attention by the research community so far. An experimental dataset is created by collecting microphone responses from 34 different mobile phones. These responses are then used to perform classification through CNNs. On different experiments, the proposed CNN is able to provide encouraging results; in particular, the achieved identification accuracy of CNNs is superior to the one obtained with more conventional machine learning algorithms like the K-nearest neighbor and support vector machine, also in the presence of additive white Gaussian noise.