Blind source computer device identification from recorded VoIP calls for forensic investigation. (March 2017)

• Record Type: Journal Article
• Authors: Jahanirad, Mehdi; Anuar, Nor Badrul; Wahab, Ainuddin Wahid Abdul
• Is Part Of: Forensic science international. Volume 272(2017)
• Page Start: 111
• Page End: 126
• Publication Date: 2017-03
• Subjects: Audio forensics -- Forensic categorization of digital devices -- Audio source device attribution -- Audio acoustic features; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2017.01.010
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: Blind source computer device identification allows tracking of recorded VoIP calls. Entropy-MFCCs extract computer device specific information from near-silent frames. The communication signal processing pipeline transformed to a control system model. The proposed application scenario was evaluated using classifier benchmarking. Abstract: The VoIP services provide fertile ground for criminal activity, thus identifying the transmitting computer devices from recorded VoIP call may help the forensic investigator to reveal useful information. It also proves the authenticity of the call recording submitted to the court as evidence. This paper extended the previous study on the use of recorded VoIP call for blind source computer device identification. Although initial results were promising but theoretical reasoning for this is yet to be found. The study suggested computing entropy of mel-frequency cepstrum coefficients (entropy-MFCC) from near-silent segments as an intrinsic feature set that captures the device response function due to the tolerances in the electronic components of individual computer devices. By applying the supervised learning techniques of naïve Bayesian, linear logistic regression, neural networks and support vector machines to the entropy-MFCC features, state-of-the-art identification accuracy of near 99.9% has been achieved on different sets of computer devices for both call recording and microphone recording scenarios. Furthermore, unsupervised learning techniques, including simple k-means, expectation-maximization and density-based spatial clustering of applications with noise (DBSCAN) provided promising results for call recording dataset by assigning the majority of instances to their correct clusters.