Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Review on steganography methods in multi-media domain

Steganography is one area in information security that is able to conceal the secret message in any media to avoid the intruders. In this paper, the review of steganography is done in certain media such as image, text, audio, and video. It analyses some of the techniques that applied steganography to discover the development of the techniques to cover a secret message. It is expected that this paper is able to describe the implementation of steganography by previous researchers on their efforts

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

Development of software for automatic sinchronization between tonalities and colours in audiovisual music therapy

This end of bachelor project consists in the automation of music and colour synchronization designed to be used in music therapy. The idea behind this concept is using colour as a new dimension to visually interpret the complex variations in music, and this project contributes to it by improving its efficiency through automation. Studies have demonstrated that the tensions in a musical piece are related to the emotion or the mood the piece transmits [1], the same way it has been proved that different colours induce a certain mood or emotion in people [2]. Those conclusions have been used by a doctorate student in the Technical University of Eindhoven to back up the proposal of making emotion the common variable between music and colour [3][4] for further music therapy purposes. This project is the technological part of the later research work mentioned. To develop it, a thorough understanding of the doctorate student’s work is required, to then to propose a software that can fulfil its demands. To start with, this paper studies Music Information Retrieval research, MIDI files format and the relation between them. This is necessary to develop a program capable of reading a MIDI file and converting it into a list of notes with their corresponding timings. Afterwards, high level music features such as chord and tonality changes are extracted, making use of music theory knowledge to reinforce MIR methods. Once the MIDI file has been interpreted into a list of chords, they are expressed as musical intervals, i.e. relative distances between them. This step is done prior to carrying out an automatic mapping of musical intervals to colours, following findings and conclusions to make a coherent matching between both disciplines [3][4]. The music to colour mapping results as a list of colours associated to timings. Finally, to visualize the outcome of the previous steps, coloured lights are changed following the colours list while the music is synchronously played. This last section is controlled by a software that establishes a connection with the lamps via Wi-Fi and executes the change-colour commands. To finish with the project, an evaluation of an external software employed is carried out using a method based on speaker diarization. Finally, conclusions regarding the expectations of the project are made, and ideas for future work and improvement are suggested.Este Trabajo Fin de Grado consiste en la automatización de la sincronización entre música y color, diseñada para fines relacionados con la musicoterapia. La idea detrás de este concepto es utilizar el color como una nueva dimensión capaz de interpretar visualmente las complejas variaciones en la música, y este proyecto contribuye a ello mejorando su eficiencia a través de la automatización. Estudios han demostrado que las tensiones que aparecen en una pieza musical están relacionadas con una emoción o estado de ánimo [1], del mismo modo que se ha demostrado que determinados colores generan una emoción o un estado en la gente [2]. Estas conclusiones han sido utilizadas por una estudiante de doctorado de la Universidad Técnica de Eindhoven para apoyar su propuesta de utilizar la emoción como la variable común entre música y color [3][4] para contribuir a los avances de la musicoterapia. Este proyecto es la parte tecnológica del trabajo de investigación que acaba de ser mencionado. Para desarrollarlo, un minucioso entendimiento de dicho trabajo es necesario, para después poder proponer un software que se ajuste sus necesidades. Para comenzar, en esta memoria se estudian las bases de ‘Music Information Retrieval’, del formato MIDI, y de la relación entre ambos campos. Este estudio es necesario para desarrollar un programa capaz de leer un archivo MIDI, y convertirlo a una lista de notas con sus correspondientes marcas en el tiempo. Después, características musicales de alto nivel, como son detección de acordes o de tonalidad, son extraídas con ayuda de conocimientos de teoría musical para reforzar los métodos de MIR. Una vez el archivo MIDI ha sido interpretado como una lista de acordes, se expresan en forma de intervalos musicales, es decir, distancias relativas entre ellos. Este paso se realiza justo antes de llevar a cabo un mapeo entre intervalos musicales y colores, siguiendo los resultados concluidos por investigación [3][4] para poder emparejar ambas disciplinas de forma coherente. El resultado de dicho mapeo es una lista de colores asociada a sus correspondientes marcas en el tiempo. Finalmente, para visualizar el resultado de los pasos anteriores, luces de colores cambian siguiendo la lista de colores mientras la música suena de forma sincronizada. Esta última sección es controlada por un software que establece una conexión con las luces vía Wi-Fi y que ejecuta los comandos cambio-de-color. Para concluir con el proyecto, se realiza una evaluación del software externo utilizado, empleando un método basado en ‘speaker diarization’. Por último, se desarrollan conclusiones respecto a las expectativas del proyecto, y se proponen ideas y mejoras para un trabajo futuro

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

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

Audio content identification

Die Entwicklung und Erforschung von inhaltsbasierenden "Music Information Retrieval (MIR)'' - Anwendungen in den letzten Jahren hat gezeigt, dass die automatische Generierung von Inhaltsbeschreibungen, die eine Identifikation oder Klassifikation von Musik oder Musikteilen ermöglichen, eine bewältigbare Aufgabe darstellt. Aufgrund der großen Massen an verfügbarer digitaler Musik und des enormen Wachstums der entsprechenden Datenbanken, werden Untersuchungen durchgeführt, die eine möglichst automatisierte Ausführung der typischen Managementprozesse von digitaler Musik ermöglichen. In dieser Arbeit stelle ich eine allgemeine Einführung in das Gebiet des ``Music Information Retrieval'' vor, insbesondere die automatische Identifikation von Audiomaterial und den Vergleich von ähnlichkeitsbasierenden Ansätzen mit reinen inhaltsbasierenden “Fingerprint”-Technologien. Einerseits versuchen Systeme, den menschlichen Hörapparat bzw. die Wahrnehmung und Definition von "Ähnlichkeit'' zu modellieren, um eine Klassifikation in Gruppen von verwandten Musiktiteln und im Weiteren eine Identifikation zu ermöglichen. Andererseits liegt der Fokus auf der Erstellung von Signaturen, die auf eine eindeutige Wiedererkennung abzielen ohne jede Aussage über ähnlich klingende Alternativen. In der Arbeit werden eine Reihe von Tests durchgeführt, die deutlich machen sollen, wie robust, zuverlässig und anpassbar Erkennungssysteme arbeiten sollen, wobei eine möglichst hohe Rate an richtig erkannten Musikstücken angestrebt wird. Dafür werden zwei Algorithmen, Rhythm Patterns, ein ähnlichkeitsbasierter Ansatz, und FDMF, ein frei verfügbarer Fingerprint-Extraktionsalgorithmus mittels 24 durchgeführten Testfällen gegenübergestellt, um die Arbeitsweisen der Verfahren zu vergleichen. Diese Untersuchungen zielen darauf ab, eine möglichst hohe Genauigkeit in der Wiedererkennung zu erreichen. Ähnlichkeitsbasierte Ansätze wie Rhythm Patterns erreichen bei der Identifikation Wiedererkennungsraten bis zu 89.53% und übertreffen in den durchgeführten Testszenarien somit den untersuchten Fingerprint-Ansatz deutlich. Eine sorgfältige Auswahl relevanter Features, die zur Berechnung von Ähnlichkeit herangezogen werden, führen zu äußerst vielversprechenden Ergebnissen sowohl bei variierten Ausschnitten der Musikstücke als auch nach erheblichen Signalveränderungen.The development and research of content-based music information retrieval (MIR) applications in the last years have shown that the generation of descriptions enabling the identification and classification of pieces of musical audio is a challenge that can be coped with. Due to the huge masses of digital music available and the growth of the particular databases, there are investigations of how to automatically perform tasks concerning the management of audio data. In this thesis I will provide a general introduction of the music information retrieval techniques, especially the identification of audio material and the comparison of similarity-based approaches with content-based fingerprint technology. On the one hand, similarity retrieval systems try to model the human auditory system in various aspects and therewith the model of perceptual similarity. On the other hand there are fingerprints or signatures which try to exactly identify music without any assessment of similarity of sound titles. To figure out the differences and consequences of using these approaches I have performed several experiments that make clear how robust and adaptable an identification system must work. Rhythm Patterns, a similarity based feature extraction scheme and FDMF, a free fingerprint algorithm have been investigated by performing 24 test cases in order to compare the principle behind. This evaluation has also been done focusing on the greatest possible accuracy. It has come out that similarity features like Rhythm Patterns are able to identify audio titles promisingly as well (i.e. up to 89.53 %) in the introduced test scenarios. The proper choice of features enables that music tracks are identified at best when focusing on the highest similarity between the candidates both for varied excerpts and signal modifications

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods