A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

This paper presents a blind audio watermarking algorithm based on the reduced singular value decomposition(RSVD). A new observation on one of the resulting unitary matrices is uncovered. The proposed scheme manipulates coefficients based on this observation in order to embed watermark bits. To preserve audio fidelity a threshold- based distortion control technique is applied and this is further supplemented by distortion suppression utilizing psychoacoustic principles. Test results on real music signals show that this watermarking scheme is in the range of imperceptibility for human hearing, is accurate and also robust against MP3 compression at various bit rates as well as other selected attacks. The data payload is comparatively high compared to existing audio watermarking schemes

A new approach for improving transparency of audio watermarking.

Chen Benrong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 125-130).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- What' s Watermarking --- p.1Chapter 1.2 --- "Information Hiding, Steganography, and Watermarking" --- p.3Chapter 1.3 --- History of Watermarking --- p.5Chapter 1.4 --- Importance of Digital Watermarking --- p.8Chapter 1.5 --- Objectives of the Thesis --- p.9Chapter 1.6 --- Thesis Outline --- p.10Chapter 2 --- Applications and Properties of Audio Watermarking --- p.12Chapter 2.1 --- Applications --- p.13Chapter 2.1.1 --- Ownership Identification and Proof --- p.13Chapter 2.1.2 --- Broadcast Monitoring --- p.16Chapter 2.1.3 --- Other Applications --- p.18Chapter 2.2 --- Properties --- p.19Chapter 2.2.1 --- Transparency --- p.20Chapter 2.2.2 --- Robustness --- p.20Chapter 2.2.3 --- Other Properties --- p.21Chapter 3 --- Possible Methods for Audio Watermarking --- p.24Chapter 3.1 --- Overview of Digital Audio Watermarking System --- p.25Chapter 3.2 --- Review of Current Methods --- p.27Chapter 3.2.1 --- Low Bit Coding --- p.27Chapter 3.2.2 --- Phase Coding --- p.28Chapter 3.2.3 --- Echo Coding --- p.29Chapter 3.2.4 --- Spread Spectrum Watermarking --- p.30Chapter 3.3 --- Other Related Approaches --- p.31Chapter 3.4 --- Outline of Proposed New Method --- p.33Chapter 4 --- Audio Watermarking System Based on Spread Spectrum --- p.36Chapter 4.1 --- Introduction --- p.36Chapter 4.2 --- Embedding and Detecting Information Bit --- p.39Chapter 4.2.1 --- General Embedding Process --- p.39Chapter 4.2.2 --- General Detection Process --- p.43Chapter 4.2.3 --- Pseudorandom Bit Sequences (PRBS) --- p.45Chapter 4.3 --- An Optimal Embedding Process --- p.48Chapter 4.3.1 --- Objective Metrics for Embedding Process --- p.48Chapter 4.3.2 --- Content Adaptive Embedding --- p.52Chapter 4.3.3 --- Determination of Frame Length L --- p.57Chapter 4.4 --- Requirement For Transparency Improvement --- p.58Chapter 5 --- Sample and Frame Selection For Transparency Improvement --- p.60Chapter 5.1 --- Introduction --- p.60Chapter 5.2 --- Sample Selection --- p.61Chapter 5.2.1 --- General Sample Selection --- p.62Chapter 5.2.2 --- Objective Evaluation Metrics --- p.65Chapter 5.2.3 --- Sample Selection For Transparency Improvement --- p.66Chapter 5.2.4 --- Theoretical Analysis of Sample Selection --- p.87Chapter 5.3 --- Frame Sclcction --- p.90Chapter 5.3.1 --- General Frame Selection --- p.91Chapter 5.3.2 --- Frame Selection For Transparency Improvement --- p.94Chapter 5.4 --- Watermark Information Retrieve --- p.103Chapter 6 --- Psychoacoustic Model For Robustness Verification --- p.105Chapter 6.1 --- Introduction of Human Auditory System --- p.106Chapter 6.1.1 --- Absolute Hearing Threshold --- p.106Chapter 6.1.2 --- Critical Bands --- p.108Chapter 6.1.3 --- Masking Effect --- p.111Chapter 6.2 --- Psychoacoustic Model of Human Auditory System --- p.112Chapter 6.3 --- Robustness Verification by Psychoacoustic Model Analysis --- p.117Chapter 7 --- Conclusions and Suggestions For Future Research --- p.121Chapter 7.1 --- Conclusions --- p.121Chapter 7.2 --- Suggestions For Future Research --- p.123Bibliography --- p.12

Digital Watermarking for Verification of Perception-based Integrity of Audio Data

In certain application fields digital audio recordings contain sensitive content. Examples are historical archival material in public archives that preserve our cultural heritage, or digital evidence in the context of law enforcement and civil proceedings. Because of the powerful capabilities of modern editing tools for multimedia such material is vulnerable to doctoring of the content and forgery of its origin with malicious intent. Also inadvertent data modification and mistaken origin can be caused by human error. Hence, the credibility and provenience in terms of an unadulterated and genuine state of such audio content and the confidence about its origin are critical factors. To address this issue, this PhD thesis proposes a mechanism for verifying the integrity and authenticity of digital sound recordings. It is designed and implemented to be insensitive to common post-processing operations of the audio data that influence the subjective acoustic perception only marginally (if at all). Examples of such operations include lossy compression that maintains a high sound quality of the audio media, or lossless format conversions. It is the objective to avoid de facto false alarms that would be expectedly observable in standard crypto-based authentication protocols in the presence of these legitimate post-processing. For achieving this, a feasible combination of the techniques of digital watermarking and audio-specific hashing is investigated. At first, a suitable secret-key dependent audio hashing algorithm is developed. It incorporates and enhances so-called audio fingerprinting technology from the state of the art in contentbased audio identification. The presented algorithm (denoted as ”rMAC” message authentication code) allows ”perception-based” verification of integrity. This means classifying integrity breaches as such not before they become audible. As another objective, this rMAC is embedded and stored silently inside the audio media by means of audio watermarking technology. This approach allows maintaining the authentication code across the above-mentioned admissible post-processing operations and making it available for integrity verification at a later date. For this, an existent secret-key ependent audio watermarking algorithm is used and enhanced in this thesis work. To some extent, the dependency of the rMAC and of the watermarking processing from a secret key also allows authenticating the origin of a protected audio. To elaborate on this security aspect, this work also estimates the brute-force efforts of an adversary attacking this combined rMAC-watermarking approach. The experimental results show that the proposed method provides a good distinction and classification performance of authentic versus doctored audio content. It also allows the temporal localization of audible data modification within a protected audio file. The experimental evaluation finally provides recommendations about technical configuration settings of the combined watermarking-hashing approach. Beyond the main topic of perception-based data integrity and data authenticity for audio, this PhD work provides new general findings in the fields of audio fingerprinting and digital watermarking. The main contributions of this PhD were published and presented mainly at conferences about multimedia security. These publications were cited by a number of other authors and hence had some impact on their works

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

Steganography and steganalysis: data hiding in Vorbis audio streams

The goal of the current work is to introduce ourselves in the world of steganography and steganalysis, centering our efforts in acoustic signals, a branch of steganography and steganalysis which has received much less attention than steganography and steganalysis for images. With this purpose in mind, it’s essential to get first a basic level of understanding of signal theory and the properties of the Human Auditory System, and we will dedicate ourselves to that aim during the first part of this work. Once established those basis, in the second part, we will obtain a precise image of the state of the art in steganographic and steganalytic sciences, from which we will be able to establish or deduce some good practices guides. With both previous subjects in mind, we will be able to create, design and implement a stego-system over Vorbis audio codec and, finally, as conclusion, analyze it using the principles studied during the first and second parts

Digital audio watermarking for broadcast monitoring and content identification

Copyright legislation was prompted exactly 300 years ago by a desire to protect authors against exploitation of their work by others. With regard to modern content owners, Digital Rights Management (DRM) issues have become very important since the advent of the Internet. Piracy, or illegal copying, costs content owners billions of dollars every year. DRM is just one tool that can assist content owners in exercising their rights. Two categories of DRM technologies have evolved in digital signal processing recently, namely digital fingerprinting and digital watermarking. One area of Copyright that is consistently overlooked in DRM developments is 'Public Performance'. The research described in this thesis analysed the administration of public performance rights within the music industry in general, with specific focus on the collective rights and broadcasting sectors in Ireland. Limitations in the administration of artists' rights were identified. The impact of these limitations on the careers of developing artists was evaluated. A digital audio watermarking scheme is proposed that would meet the requirements of both the broadcast and collective rights sectors. The goal of the scheme is to embed a standard identifier within an audio signal via modification of its spectral properties in such a way that it would be robust and perceptually transparent. Modification of the audio signal spectrum was attempted in a variety of ways. A method based on a super-resolution frequency identification technique was found to be most effective. The watermarking scheme was evaluated for robustness and found to be extremely effective in recovering embedded watermarks in music signals using a semi-blind decoding process. The final digital audio watermarking algorithm proposed facilitates the development of other applications in the domain of broadcast monitoring for the purposes of equitable royalty distribution along with additional applications and extension to other domains

ИНТЕЛЛЕКТУАЛЬНЫЙ числовым программным ДЛЯ MIMD-компьютер

For most scientific and engineering problems simulated on computers the solving of problems of the computational mathematics with approximately given initial data constitutes an intermediate or a final stage. Basic problems of the computational mathematics include the investigating and solving of linear algebraic systems, evaluating of eigenvalues and eigenvectors of matrices, the solving of systems of non-linear equations, numerical integration of initial- value problems for systems of ordinary differential equations.Для більшості наукових та інженерних задач моделювання на ЕОМ рішення задач обчислювальної математики з наближено заданими вихідними даними складає проміжний або остаточний етап. Основні проблеми обчислювальної математики відносяться дослідження і рішення лінійних алгебраїчних систем оцінки власних значень і власних векторів матриць, рішення систем нелінійних рівнянь, чисельного інтегрування початково задач для систем звичайних диференціальних рівнянь.Для большинства научных и инженерных задач моделирования на ЭВМ решение задач вычислительной математики с приближенно заданным исходным данным составляет промежуточный или окончательный этап. Основные проблемы вычислительной математики относятся исследования и решения линейных алгебраических систем оценки собственных значений и собственных векторов матриц, решение систем нелинейных уравнений, численного интегрирования начально задач для систем обыкновенных дифференциальных уравнений