Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Sécurité de l’information par stéganographie basée sur les séquences chaotiques

Steganography is the art of the dissimulation of a secret message in a cover medium such that the resultant medium (stego) is almost identical to the cover medium. Nowadays, with the globalization of the exchanges (Internet, messaging and e-commerce), using diverse mediums (sound, embellish with images, video), modern steganography is widely expanded. In this manuscript, we studied adaptive LSB methods of stéganography in spatial domain and frequency domain (DCT, and DWT), allowing of hiding the maximum of useful information in a cover image, such that the existence of the secret message in the stégo image is imperceptible and practically undetectable. Security of the message contents, in the case of its detection by an opponent, is not really insured by the methods proposed in the literature. To solve this question, we adapted and implemented two (known) methods of adaptive stéganographie LSB, by adding a strong chaotic system allowing a quasi-chaotic insertion of the bits of the secret message. The proposed chaotic system consists of a generator of strong chaotic sequences, supplying the dynamic keys of a modified chaotic 2D Cat map. Universal steganalysis (classification) of the developed methods of stéganography, is studied. On this question, we used the linear discriminating analysis of Fisher as classifier of the characteristic vectors of Farid, Shi and Wang. This choice is based on the wide variety of tested characteristic vectors that give an information about the properties of the image before and after message insertion. An analysis of the performances of three developed methods of steganalysis, applied to the produced stego images by the proposed adaptive methods of stéganography, is realized. Performance evaluation of the classification is realized by using the parameters: sensibility, specificity, precision and coefficient Kappa.La stéganographie est l’art de la dissimulation de l’information secrète dans un médium donné (cover) de sorte que le médium résultant (stégo) soit quasiment identique au médium cover. De nos jours, avec la mondialisation des échanges (Internet, messagerie et commerce électronique), s’appuyant sur des médiums divers (son, image, vidéo), la stéganographie moderne a pris de l’ampleur. Dans ce manuscrit, nous avons étudié les méthodes de stéganographie LSB adaptatives, dans les domaines spatial et fréquentiel (DCT, et DWT), permettant de cacher le maximum d’information utile dans une image cover, de sorte que l’existence du message secret dans l’image stégo soit imperceptible et pratiquement indétectable. La sécurité du contenu du message, dans le cas de sa détection par un adversaire, n’est pas vraiment assurée par les méthodes proposées dans la littérature. Afin de résoudre cette question, nous avons adapté et implémenté deux méthodes (connues) de stéganographie LSB adaptatives, en ajoutant un système chaotique robuste permettant une insertion quasi-chaotique des bits du message secret. Le système chaotique proposé consiste en un générateur de séquences chaotiques robustes fournissant les clés dynamiques d’une carte Cat 2-D chaotique modifiée. La stéganalyse universelle (classification) des méthodes de stéganographie développées est étudiée. A ce sujet, nous avons utilisé l’analyse discriminante linéaire de Fisher comme classifieur des vecteurs caractéristiques de Farid, Shi et Wang. Ce choix est basé sur la large variété de vecteurs caractéristiques testés qui fournissent une information sur les propriétés de l’image avant et après l’insertion du message. Une analyse des performances des trois méthodes de stéganalyse développées, appliquées sur des images stégo produites par les deux méthodes de stéganographie LSB adaptatives proposées, est réalisée. L’évaluation des résultats de la classification est réalisée par les paramètres: sensibilité, spécificité, précision et coefficient Kappa