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%

An evaluation of entropy measures for microphone identification

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented

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

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

Physical Layer Identification and authentication of electronic devices

In this thesis, I have investigated the problem of identification and authentication of electronic devices through their physical layer intrinsic features or fingerprints. The concept is that small differences in the electronic components of electronic devices leave small but significant traces in the digital output generated by the electronic device. Then, an analysis of the digital output provides the capability to identify and/or authenticate an electronic device from its digital output with a degree of accuracy, which is based on various factors including environmental effects. This research area has become more prominent in recent times due to the increasing computing power available for signal processing and analysis, which allows a more efficient and accurate extraction of the fingerprints. Even if there is considerable research in this area, which has proven the concept both with theoretical analysis and experimental results, there are still many aspects to be investigated both for the different types of electronic devices and for the analysis of the digital output through signal processing and machine learning techniques. The PhD activities have investigated various novel aspects in comparison to the existing literature. This thesis describes most of the results and describes the novelty in comparison to previous research literature. Three specific use cases were considered: identification of wireless devices, microphones and magnetometers

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

A speaker classification framework for non-intrusive user modeling : speech-based personalization of in-car services

Speaker Classification, i.e. the automatic detection of certain characteristics of a person based on his or her voice, has a variety of applications in modern computer technology and artificial intelligence: As a non-intrusive source for user modeling, it can be employed for personalization of human-machine interfaces in numerous domains. This dissertation presents a principled approach to the design of a novel Speaker Classification system for automatic age and gender recognition which meets these demands. Based on literature studies, methods and concepts dealing with the underlying pattern recognition task are developed. The final system consists of an incremental GMM-SVM supervector architecture with several optimizations. An extensive data-driven experiment series explores the parameter space and serves as evaluation of the component. Further experiments investigate the language-independence of the approach. As an essential part of this thesis, a framework is developed that implements all tasks associated with the design and evaluation of Speaker Classification in an integrated development environment that is able to generate efficient runtime modules for multiple platforms. Applications from the automotive field and other domains demonstrate the practical benefit of the technology for personalization, e.g. by increasing local danger warning lead time for elderly drivers.Die Sprecherklassifikation, also die automatische Erkennung bestimmter Merkmale einer Person anhand ihrer Stimme, besitzt eine Vielzahl von Anwendungsmöglichkeiten in der modernen Computertechnik und Künstlichen Intelligenz: Als nicht-intrusive Wissensquelle für die Benutzermodellierung kann sie zur Personalisierung in vielen Bereichen eingesetzt werden. In dieser Dissertation wird ein fundierter Ansatz zum Entwurf eines neuartigen Sprecherklassifikationssystems zur automatischen Bestimmung von Alter und Geschlecht vorgestellt, welches diese Anforderungen erfüllt. Ausgehend von Literaturstudien werden Konzepte und Methoden zur Behandlung des zugrunde liegenden Mustererkennungsproblems entwickelt, welche zu einer inkrementell arbeitenden GMM-SVM-Supervector-Architektur mit diversen Optimierungen führen. Eine umfassende datengetriebene Experimentalreihe dient der Erforschung des Parameterraumes und zur Evaluierung der Komponente. Weitere Studien untersuchen die Sprachunabhängigkeit des Ansatzes. Als wesentlicher Bestandteil der Arbeit wird ein Framework entwickelt, das alle im Zusammenhang mit Entwurf und Evaluierung von Sprecherklassifikation anfallenden Aufgaben in einer integrierten Entwicklungsumgebung implementiert, welche effiziente Laufzeitmodule für verschiedene Plattformen erzeugen kann. Anwendungen aus dem Automobilbereich und weiteren Domänen demonstrieren den praktischen Nutzen der Technologie zur Personalisierung, z.B. indem die Vorlaufzeit von lokalen Gefahrenwarnungen für ältere Fahrer erhöht wird

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Technology and Testing

From early answer sheets filled in with number 2 pencils, to tests administered by mainframe computers, to assessments wholly constructed by computers, it is clear that technology is changing the field of educational and psychological measurement. The numerous and rapid advances have immediate impact on test creators, assessment professionals, and those who implement and analyze assessments. This comprehensive new volume brings together leading experts on the issues posed by technological applications in testing, with chapters on game-based assessment, testing with simulations, video assessment, computerized test development, large-scale test delivery, model choice, validity, and error issues. Including an overview of existing literature and ground-breaking research, each chapter considers the technological, practical, and ethical considerations of this rapidly-changing area. Ideal for researchers and professionals in testing and assessment, Technology and Testing provides a critical and in-depth look at one of the most pressing topics in educational testing today