Audio recording system leaves its characteristic artifacts in the recordings made through it which are used to link an audio recording to “the” microphone used. Microphone recognition method is a process in which microphone is recognized from the audio recorded using the characteristic artifacts. This paper focuses on the performance analysis of microphone identification algorithms in the presence of splicing attack. Statistical pattern recognition based method for blind detection method for microphone identification is used for this study.  Performance of selected method is evaluated both in the presence and in the absence of splicing attack. Experimental results indicate that the selected method fail to detect any forgeries less than 20% of the length of the recording.

Authors: Hafeez, Azeem; Malik, Hafiz; Mahmood, Khalid
Affiliations: University of Michigan, Dearborn, MI, USA; Oakland University, Oakland, CA, USA(See document for exact affiliation information.)
AES Conference: 2017 AES International Conference on Audio Forensics (June 2017)
Paper Number: 4-2
Publication Date: June 6, 2017 Import into BibTeX
Subject: Signal Analysis
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18749

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