Survey of the Use of Steganography over the Internet

This paper addressesthe use of Steganography over the Internet by terrorists. There were ru-mors in the newspapers that Steganography is being used to covert communication between terrorists, without presenting any scientific proof. Niels Provos and Peter Honeyman conducted an extensive Internet search where they analyzed over 2 million images and didn’t find a single hidden image. After this study the scientific community was divided: some believed that Niels Provos and Peter Honeyman was conclusive enough other did not. This paper describes what Steganography is and what can be used for, various Steganography techniques and also presents the studies made regarding the use of Steganography on the Internet.Steganography, Secret Communication, Information Hiding, Cryptography

Steganalysis Techniques: A Comparative Study

Steganography is the art of hiding information within cover objects like images or audio/video files. It has been widely reported that there has been a surge in the use of steganography for criminal activities and therefore, implementing effective detection techniques is an essential task in digital forensics. Unfortunately, building a single effective detection technique still remains one of the biggest challenges. This report presents a comparative study of three steganalysis techniques. We investigated and compared the performances of each technique in the detection of embedding methods considered. Based on the results of our analysis, we provide information as to which specific steganalysis technique needs to be used for a particular steganographic method. Finally, we propose a procedure which may help a forensic examiner to decide an order in which different steganalysis techniques need to be considered in the detection process to achieve the best detection results in terms of both time and accuracy

Steganalysis Techniques: A Comparative Study

Steganography is the art of hiding information within cover objects like images or audio/video files. It has been widely reported that there has been a surge in the use of steganography for criminal activities and therefore, implementing effective detection techniques is an essential task in digital forensics. Unfortunately, building a single effective detection technique still remains one of the biggest challenges. This report presents a comparative study of three steganalysis techniques. We investigated and compared the performances of each technique in the detection of embedding methods considered. Based on the results of our analysis, we provide information as to which specific steganalysis technique needs to be used for a particular steganographic method. Finally, we propose a procedure which may help a forensic examiner to decide an order in which different steganalysis techniques need to be considered in the detection process to achieve the best detection results in terms of both time and accuracy

Research on digital image watermark encryption based on hyperchaos

The digital watermarking technique embeds meaningful information into one or more watermark images hidden in one image, in which it is known as a secret carrier. It is difficult for a hacker to extract or remove any hidden watermark from an image, and especially to crack so called digital watermark. The combination of digital watermarking technique and traditional image encryption technique is able to greatly improve anti-hacking capability, which suggests it is a good method for keeping the integrity of the original image. The research works contained in this thesis include: (1)A literature review the hyperchaotic watermarking technique is relatively more advantageous, and becomes the main subject in this programme. (2)The theoretical foundation of watermarking technologies, including the human visual system (HVS), the colour space transform, discrete wavelet transform (DWT), the main watermark embedding algorithms, and the mainstream methods for improving watermark robustness and for evaluating watermark embedding performance. (3) The devised hyperchaotic scrambling technique it has been applied to colour image watermark that helps to improve the image encryption and anti-cracking capabilities. The experiments in this research prove the robustness and some other advantages of the invented technique. This thesis focuses on combining the chaotic scrambling and wavelet watermark embedding to achieve a hyperchaotic digital watermark to encrypt digital products, with the human visual system (HVS) and other factors taken into account. This research is of significant importance and has industrial application value

Survey of the Use of Steganography over the Internet

This paper addressesthe use of Steganography over the Internet by terrorists. There were ru-mors in the newspapers that Steganography is being used to covert communication between terrorists, without presenting any scientific proof. Niels Provos and Peter Honeyman conducted an extensive Internet search where they analyzed over 2 million images and didn’t find a single hidden image. After this study the scientific community was divided: some believed that Niels Provos and Peter Honeyman was conclusive enough other did not. This paper describes what Steganography is and what can be used for, various Steganography techniques and also presents the studies made regarding the use of Steganography on the Internet

PIRANHA: an engine for a methodology of detecting covert communication via image-based steganography

In current cutting-edge steganalysis research, model-building and machine learning has been utilized to detect steganography. However, these models are computationally and cognitively cumbersome, and are specifically and exactly targeted to attack one and only one type of steganography. The model built and utilized in this thesis has shown capability in detecting a class or family of steganography, while also demonstrating that it is viable to construct a minimalist model for steganalysis. The notion of detecting steganographic primitives or families is one that has not been discussed in literature, and would serve well as a first-pass steganographic detection methodology. The model built here serves this end well, and it must be kept in mind that the model presented is posited to work as a front-end broad-pass filter for some of the more computationally advanced and directed stganalytic algorithms currently in use. This thesis attempts to convey a view of steganography and steganalysis in a manner more utilitarian and immediately useful to everyday scenarios. This is vastly different from a good many publications that treat the topic as one relegated only to cloak-and-dagger information passing. The subsequent view of steganography as primarily a communications tool useable by petty information brokers and the like directs the text and helps ensure that the notion of steganography as a digital dead-drop box is abandoned in favor of a more grounded approach. As such, the model presented underperforms specialized models that have been presented in current literature, but also makes use of a large image sample space (747 images) as well as images that are contextually diverse and representative of those seen in wide use. In future applications by either law-enforcement or corporate officials, it is hoped that the model presented in this thesis can aid in rapid and targeted responses without causing undue strain upon an eventual human operator. As such, a design constraint that was utilized for this research favored a False Negative as opposed to a False Positive - this methodology helps to ensure that, in the event of an alert, it is worthwhile to apply a more directed attack against the flagged image

Coverless image steganography using morphed face recognition based on convolutional neural network

In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness

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

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest