Sweep-to-Unlock: Fingerprinting Smartphones Based on Loudspeaker Roll-Off Characteristics

Adriana Berdich, Bogdan Groza, Rene Mayrhofer, Efrat Levy, Asaf Shabtai, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Fingerprinting smartphones based on acoustic characteristics of their loudspeaker may have a number of applications in device-to-device authentication as well as in forensic investigations. In this work we propose an efficient fingerprinting methodology by using the roll-off characteristics of the device speaker, i.e., the transition between the low and high stopbands to the passband segment of the speaker. We extract roll-off characteristics from sweep signals, also know as chirps, that are commonly used in practice to test speaker response. This procedure appears to be more stable against variations of the volume level and allows the use of simple linear approximations, which are intuitive and easy to compute, in order to extract the fingerprint. To increase detection accuracy, on the basis of the proven performance of deep learning techniques, a convolutional and a bi-directional long short term memory neural network are further proposed and their performance demonstrated for authentication purposes. While numerous applications may be envisioned, we specifically focus on the use of speaker characteristics in relation to in-vehicle infotainment units, checking if recordings from these units can be used to fingerprint a specific phone.

Original languageEnglish
Pages (from-to)2417-2434
Number of pages18
JournalIEEE Transactions on Mobile Computing
Volume22
Issue number4
DOIs
StatePublished - 1 Apr 2023

Keywords

  • Sweep signal
  • fingerprinting
  • loudspeakers
  • machine learning
  • smartphones

ASJC Scopus subject areas

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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