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 Based Audio Forensic on Identical Microphones

Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collected from two microphones of the same model. Experimental results and analysis indicate that the signal of sound recording of identical microphone is not exactly same and the difference is up to 1% - 3%

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

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.Comment: 5 page

Microphone smart device fingerprinting from video recordings

This report aims at summarizing the on-going research activity carried out by DG-JRC in the framework of the institutional project Authors and Victims Identification of Child Abuse on-line, concerning the use of microphone fingerprinting for source device classification. Starting from an exhaustive study of the State of Art regarding the matter, this report describes a feasibility study about the adoption of microphone fingerprinting for source identification of video recordings. A set of operational scenarios have been established in collaboration with EUROPOL law enforcers, according to investigators needs. A critical analysis of the obtained results has demonstrated the feasibility of microphone fingerprinting and it has suggested a set of recommendations, both in terms of usability and future researches in the field.JRC.E.3-Cyber and Digital Citizens' Securit