Fingerprinting Smartphones Based on Microphone Characteristics From Environment Affected Recordings

Fingerprinting devices based on unique characteristics of their sensors is an important research direction nowadays due to its immediate impact on non-interactive authentications and no less due to privacy implications. In this work, we investigate smartphone fingerprints obtained from microphone data based on recordings containing human speech, environmental sounds and several live recordings performed outdoors. We record a total of 19,200 samples using distinct devices as well as identical microphones placed on the same device in order to check the limits of the approach. To comply with real-world circumstances, we also consider the presence of several types of noise that is specific to the scenarios which we address, e.g., traffic and market noise at distinct volumes, and may reduce the reliability of the data. We analyze several classification techniques based on traditional machine learning algorithms and more advanced deep learning architectures that are put to test in recognizing devices from the recordings they made. The results indicate that the classical Linear Discriminant classifier and a deep-learning Convolutional Neural Network have comparable success rates while outperforming all the rest of the classifiers.