Electrical Network Frequency as a Tool for Audio Concealment Process

[[abstract]]We live in a digital era. Digital contents may be produced by digital equipments or by converting old analog recordings. With the rapid growth of digital contents, digital archiving technology is demanded. Different types of contents require different processing techniques. In this paper, we focus on digital audio contents. The related techniques, such as forensics, authentication, and error concealment, were studied. When converting audio tapes to digital files, sometimes a certain automatic error detection and concealment is needed. However, traditional audio tapes were recorded without any error recovery information. Based on the restriction, we proposed a scheme that incorporates the electrical network frequency (ENF) as a tool for detecting damaged audio segments. The goal is to help people identifying candidate concealment segments. When using in an archiving application, it reduces the manpower as well as increases the accuracy of the generated meta-data.[[conferencetype]]國際[[conferencedate]]20101015~20101017[[iscallforpapers]]Y[[conferencelocation]]Darmstadt, German

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

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Practice-Oriented Privacy in Cryptography

While formal cryptographic schemes can provide strong privacy guarantees, heuristic schemes that prioritize efficiency over formal rigor are often deployed in practice, which can result in privacy loss. Academic schemes that do receive rigorous attention often lack concrete efficiency or are difficult to implement. This creates tension between practice and research, leading to deployed privacy-preserving systems that are not backed by strong cryptographic guarantees. To address this tension between practice and research, we propose a practice-oriented privacy approach, which focuses on designing systems with formal privacy models that can effectively map to real-world use cases. This approach includes analyzing existing privacy-preserving systems to measure their privacy guarantees and how they are used. Furthermore, it explores solutions in the literature and analyzes gaps in their models to design augmented systems that apply more clearly to practice. We focus on two settings of privacy-preserving payments and communications. First, we introduce BlockSci, a software platform that can be used to perform analyses on the privacy and usage of blockchains. Specifically, we assess the privacy of the Dash cryptocurrency and analyze the velocity of cryptocurrencies, finding that Dash’s PrivateSend may still be vulnerable to clustering attacks and that a significant fraction of transactions on Bitcoin are “self-churn” transactions. Next, we build a technique for reducing bandwidth in mixing cryptocurrencies, which suffer from a practical limitation: the size of the transaction growing linearly with the size of the anonymity set. Our proposed technique efficiently samples cover traffic from a finite and public set of known values, while deriving a compact description of the resulting transaction set. We show how this technique can be integrated with various currencies and different cover sampling distributions. Finally, we look at the problem of establishing secure communication channels without access to a trusted public key infrastructure. We construct a scheme that uses network latency and reverse turing tests to detect the presence of eavesdroppers, prove our construction secure, and implement it on top of an existing communication protocol. This line of work bridges the gap between theoretical cryptographic research and real-world deployments to bring better privacy-preserving schemes to end users

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

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers