Crypto Steganography using linear algebraic equation

Demand of information security is increasing day by day with the exponential growth of Internet. The content of message is kept secret in cryptography, where as steganography message is embedded into the cover image. In this paper a system is developed in which cryptography and steganography are used as integrated part along with newly developed enhanced security model. In cryptography the process of encryption is carried out using symmetric block ciphers with linear algebraic equation to encrypt a message [1] and the obtained cipher text is hidden in to the cover image which makes the system highly secured. Least Significant Bit (LSB) technique is used for message hiding which replaces the least significant Bits of pixel selected to the hide the information. A large number of commercial steganographic programs use LSB as the method of choice for message hiding in 24-bit,8bit-color images, and gray scale images. It is observed from the simulation study that both methods together enhance security significantly

Review Paper on Image and Video Based Steganography

مع التطور الكبير في تقنيات المعلومات الإلكترونية والشبكات ، وفي ضوء بعض الظروف التي تفرض العمل على الإنترنت ، والتي تعتبر بيئة غير آمنة للمعلومات السرية ، أصبح الحفاظ على أمن البيانات ذات أولوية مهمة. من أهم التقنيات المستخدمة للحفاظ على سرية البيانات المنقولة وعدم إخضاعها للمهاجمين هي الإخفاء والتشفير ، وتعتمد هاتان التقنيتان أيضًا على الوسيط الذي ينقل البيانات السرية ، سواء كان ملف صورة أو صوت أو فيديو. علم إخفاء المعلومات هو علم دمج البيانات الرقمية بطريقة لا يمكن لأحد الشك في وجودها. التشفير هو تقنية أخرى تستخدم لحماية البيانات ، عند استخدامه مع إخفاء المعلومات يزيد من قوة حماية المعلومات. تقدم هذه الورقة لمحة عامة عن تقنيات إخفاء المعلومات النصية داخل ملفات الصور والفيديو. تهدف هذه الدراسة إلى تقديم ملخص للطريقة الأكثر أمانًا وأمانًا وقدرة حمولتها على إخفاء البيانات.With the great development in electronic and network information technologies, and in light of some circumstances that impose work on the Internet, which is considered to be an insecure environment for confidential information, therefore maintaining data security has become an important priority. The most important techniques used to maintain the confidentiality of the transferred data and not subject it to attackers are concealment and encryption, and these two technologies also depend on the medium that transmits the secret data, whether it is an image file, sound or video. Steganography is the science of embedding digital data in such a way no one can doubt its existence. Encryption is another technology used to protect data, when used with steganography increases the power of information protection. This paper provides an overview of techniques for hiding textual information within image and video files. This study aims to provide a summary of the method that is safer, more secure, and the ability of its payload to hide data

Steganography and Steganalysis in Digital Multimedia: Hype or Hallelujah?

In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed.In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed

Human visual based perception of steganographic images

In 2014 it was estimated that 1.8 billion images were uploaded daily to the Internet, and in 2018 it is estimated that 3.2 billion images are shared daily. Some of these uploaded images may contain hidden information that can potentially be malicious (e.g. an image that contains hidden information regarding terrorism recruitment) or may cause serious damage (e.g. an employee wishing to hide sensitive company details in an image file and exporting the image to third parties). This research studied the most effective methods in manipulating images to hide information (Data Loss). Significant work has been done on computational algorithmic detection. Yet the desired output from this work was to find the point at which a human can no longer visually establish the difference between an original image and a manipulated image. This research examines the extent of use for file formats, bit depth alterations, least significant bits, message and audio concealment and watermark and filtering techniques for image steganography. The findings of this study indicated that audio insertion and picture insertion into cover image files are the strongest in deceiving the human eye. These results have been categorised for human visual perception in image-based steganography.PostprintPeer reviewe

Zaštitna grafika poštanskih maraka dualnim svojstvima bojila sa parametrom Z i individualiziranim rasterskim elementom

U radu se postavljaju visoki standardi dizajna za tehnološku izvedu poštanskih maraka kao vrijednosnica na bazi teorije Infraredesigna. Zbog stalne potrebe za novim zaštitama u borbi protiv krivotvorenja, tisak je podređen korištenju inovacija i patenata. Rad je posvećen višestrukoj zaštiti poštanskih maraka, kako bi se osigurala njihova autentičnost. Kreiraju se zaštitni elementi temeljeni na tehnologiji skrivanja infracrvene Z slike unutar vidljive slike. Modeliraju se rasterski elementi koji sudjeluju u kreiranju grafike u proširenom spektru od 400 do 1000nm. Konvencionalno rastriranje se zamjenjuje individualizirano dizajniranim rasterskim elementima kako bi se postigla raspršenost rubova skrivene slike te na taj način unaprijedilo sakrivanje čime se potvrđuje hipoteza da rasterski element sa bitmapiranim elementom slike onemogućava krivotvorenje. Dizajn nove poštanske marke se izvodi planiranim spot bojama sa Z parametrom. Izvode se detaljni proračuni novih Z spot bojila za prošireni spektar u uvjetima ofsetnog tiska. Eksperimentalno je postavljena i dokazana osmerobojna spot CMYKIR separacija sa četiri VS/IR blizanca boja. Definiraju se nove faze dizajna za potrebe kreiranja dvostruke informacije. Daju se novi algoritmi u optimizaciji grafika za vidljivi i infracrveni dio spektra. Izvedeno je šest modela CMYKIR separacije koji odgovaraju na specifične probleme na koje se nailazi u različitim zahtjevima dizajna. Kroz eksperimentalne primjere poštanskih maraka se detaljno analiziraju problemi kod realizacije otisaka sa skrivenom grafikom. Na njih je primijenjena proširena teorija CMYKIR separacije. Kombiniranjem takvih grafika sa individualiziranim rasterima dolazi se do potpunog skrivanja infracrvene informacije u vidljivom dijelu spektra te se postiže vrhunska zaštita poštanske marke, čime se potvrđuje hipoteza o nemogućnosti krivotvorenja. Ujedno se udovoljava i visokim dizajnerskim zahtjevima za kreiranjem bogatih grafika te se postiže dvostruko iskorištenje ograničenih dimenzija poštanske marke

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

Robust steganographic techniques for secure biometric-based remote authentication

Biometrics are widely accepted as the most reliable proof of identity, entitlement to services, and for crime-related forensics. Using biometrics for remote authentication is becoming an essential requirement for the development of knowledge-based economy in the digital age. Ensuring security and integrity of the biometric data or templates is critical to the success of deployment especially because once the data compromised the whole authentication system is compromised with serious consequences for identity theft, fraud as well as loss of privacy. Protecting biometric data whether stored in databases or transmitted over an open network channel is a serious challenge and cryptography may not be the answer. The main premise of this thesis is that Digital Steganography can provide an alternative security solutions that can be exploited to deal with the biometric transmission problem. The main objective of the thesis is to design, develop and test steganographic tools to support remote biometric authentication. We focus on investigating the selection of biometrics feature representations suitable for hiding in natural cover images and designing steganography systems that are specific for hiding such biometric data rather than being suitable for general purpose. The embedding schemes are expected to have high security characteristics resistant to several types of steganalysis tools and maintain accuracy of recognition post embedding. We shall limit our investigations to embedding face biometrics, but the same challenges and approaches should help in developing similar embedding schemes for other biometrics. To achieve this our investigations and proposals are done in different directions which explain in the rest of this section. Reviewing the literature on the state-of-art in steganography has revealed a rich source of theoretical work and creative approaches that have helped generate a variety of embedding schemes as well as steganalysis tools but almost all focused on embedding random looking secrets. The review greatly helped in identifying the main challenges in the field and the main criteria for success in terms of difficult to reconcile requirements on embedding capacity, efficiency of embedding, robustness against steganalysis attacks, and stego image quality. On the biometrics front the review revealed another rich source of different face biometric feature vectors. The review helped shaping our primary objectives as (1) identifying a binarised face feature factor with high discriminating power that is susceptible to embedding in images, (2) develop a special purpose content-based steganography schemes that can benefit from the well-defined structure of the face biometric data in the embedding procedure while preserving accuracy without leaking information about the source biometric data, and (3) conduct sufficient sets of experiments to test the performance of the developed schemes, highlight the advantages as well as limitations, if any, of the developed system with regards to the above mentioned criteria. We argue that the well-known LBP histogram face biometric scheme satisfies the desired properties and we demonstrate that our new more efficient wavelet based versions called LBPH patterns is much more compact and has improved accuracy. In fact the wavelet version schemes reduce the number of features by 22% to 72% of the original version of LBP scheme guaranteeing better invisibility post embedding. We shall then develop 2 steganographic schemes. The first is the LSB-witness is a general purpose scheme that avoids changing the LSB-plane guaranteeing robustness against targeted steganalysis tools, but establish the viability of using steganography for remote biometric-based recognition. However, it may modify the 2nd LSB of cover pixels as a witness for the presence of the secret bits in the 1st LSB and thereby has some disadvantages with regards to the stego image quality. Our search for a new scheme that exploits the structure of the secret face LBPH patterns for improved stego image quality has led to the development of the first content-based steganography scheme. Embedding is guided by searching for similarities between the LBPH patterns and the structure of the cover image LSB bit-planes partitioned into 8-bit or 4-bit patterns. We shall demonstrate the excellent benefits of using content-based embedding scheme in terms of improved stego image quality, greatly reduced payload, reduced lower bound on optimal embedding efficiency, robustness against all targeted steganalysis tools. Unfortunately our scheme was not robust against the blind or universal SRM steganalysis tool. However we demonstrated robustness against SRM at low payload when our scheme was modified by restricting embedding to edge and textured pixels. The low payload in this case is sufficient to embed a secret full face LBPH patterns. Our work opens new exciting opportunities to build successful real applications of content-based steganography and presents plenty of research challenges