A Context Model for Microphone Forensics and its Application in Evaluations

ABSTRACT In this paper we first design a suitable context model for microphone recordings, formalising and describing the involved signal processing pipeline and the corresponding influence factors. As a second contribution we apply the context model to devise empirical investigations about: a) the identification of suitable classification algorithms for statistical pattern recognition based microphone forensics, evaluating 74 supervised classification techniques and 8 clusterers; b) the determination of suitable features for the pattern recognition (with very good results for second order derivative MFCC based features), showing that a reduction to the 20 best features has no negative influence to the classification accuracy, but increases the processing speed by factor 30; c) the determination of the influence of changes in the microphone orientation and mounting on the classification performance, showing that the first has no detectable influence, while the latter shows a strong impact under certain circumstances; d) the performance achieved in using the statistical pattern recognition based microphone forensics approach for the detection of audio signal compositions. MOTIVATION AND INTRODUCTION The past years have seen significant advances in digital image forensics. An overview of currently established authentication approaches for this domain is given by Hany Farid 5 . In contrast to image forensics, in the field of audio forensics so far only a limited number of approaches can be found, even though audio forensics can be considered to be very interesting for application scenarios where trust in authenticity and integrity of audio signals might be required, e.g. for evidences in court cases or in the ingest phase of secure digital long term archives. The currently existing approaches for microphone forensics (MF; a.k.a. recording forensics or recording source forensics) -as one of the most important sub-categories in audio forensics, can be classified into three classes: ENF-based approaches: One quite mature, but physically complex approach found in literature (e.g. Grigoras 7 ) is the usage of the electric network frequency (ENF) in recordings to evaluate digital audio authenticity. The complex electrophysical requirements for this approach are summarized by Grigoras et al. Time domain and local phenomena based evaluations: In 2010 Malik and Farid 2 describe a technique to model and estimate the amount of reverberation in an audio recording. Because reverberation depends on the shape and composition of a room, differences in the estimated reverberation can be used in a forensic setting for authentication. The usage of similar characteristics can be found in closely related research fields like e.g. in the works from Maher 9 on gunshot characterization. Yang et al. In this paper we extend the current state-of-the-art by investigations work described by Oermann et al. 14 and Kraetzer et al. 1 . As a first important step we design a suitable context model for microphone recordings, formalising and describing the involved 5-stage recording process pipeline. Second, we apply the context model to devise empirical investigations aiming at the generation of required domain knowledge. These questions about the provenance, persistence and uniqueness of a sensor patterns in microphones are raised by previous work in this fiel

Statistical pattern recognition for audio-forensics : empirical investigations on the application scenarios audio steganalysis and microphone forensics

Magdeburg, Univ., Fak. für Informatik, Diss., 2013von Christian Krätze