Natural Image Statistics for Digital Image Forensics

We describe a set of natural image statistics that are built upon two multi-scale image decompositions, the quadrature mirror filter pyramid decomposition and the local angular harmonic decomposition. These image statistics consist of first- and higher-order statistics that capture certain statistical regularities of natural images. We propose to apply these image statistics, together with classification techniques, to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection. We also apply these image statistics to the traditional art authentication for forgery detection and identification of artists in an art work. For each application we show the effectiveness of these image statistics and analyze their sensitivity and robustness

An Overview of Steganography for the Computer Forensics Examiner (Updated Version, February 2015)

Steganography is the art of covered or hidden writing. The purpose of steganography is covert communication-to hide the existence of a message from a third party. This paper is intended as a high-level technical introduction to steganography for those unfamiliar with the field. It is directed at forensic computer examiners who need a practical understanding of steganography without delving into the mathematics, although references are provided to some of the ongoing research for the person who needs or wants additional detail. Although this paper provides a historical context for steganography, the emphasis is on digital applications, focusing on hiding information in online image or audio files. Examples of software tools that employ steganography to hide data inside of other files as well as software to detect such hidden files will also be presented. An edited version originally published in the July 2004 issues of Forensic Science Communications

Pokročilé metody detekce steganografického obsahu

Steganography can be used for illegal activities. It is essential to be prepared. To detect steganography images, we have a counter-technique known as steganalysis. There are different steganalysis types, depending on if the original artifact (cover work) is known or not, or we know which algorithm was used for embedding. In terms of practical use, the most important are “blind steganalysis” methods that can be applied to image files because we do not have the original cover work for comparison. This philosophiæ doctor thesis describes the methodology to the issues of image steganalysis.In this work, it is crucial to understand the behavior of the targeted steganography algorithm. Then we can use it is weaknesses to increase the detection capability and success of categorization. We are primarily focusing on breaking the steganography algorithm OutGuess2.0. and secondary on breaking the F5 algorithm. We are analyzing the detector's ability, which utilizes a calibration process, blockiness calculation, and shallow neural network, to detect the presence of steganography message in the suspected image. The new approach and results are discussed in this Ph.D. thesis.Steganografie může být využita k nelegálním aktivitám. Proto je velmi důležité být připraven. K detekci steganografického obrázku máme k dispozici techniku známou jako stegoanalýza. Existují různé typy stegoanalýzy v závislosti na tom, zda je znám originální nosič nebo zdali víme, jaký byl použit algoritmus pro vložení tajné zprávy. Z hlediska praktického použití jsou nejdůležitější metody "slepé stagoanalýzy", které zle aplikovat na obrazové soubory a jelikož nemáme originální nosič pro srovnání. Tato doktorská práce popisuje metodologii obrazové stegoanalýzy. V této práci je důležité porozumět chování cíleného steganografického algoritmu. Pak můžeme využít jeho slabiny ke zvýšení detekční schopnosti a úspěšnosti kategorizace. Primárně se zaměřujeme na prolomení steganografického algoritmu OutGuess2.0 a sekundárně na algoritmus F5. Analyzujeme schopnost detektoru, který využívá proces kalibrace, výpočtu shlukování a mělkou neuronovou síť k detekci přítomnosti steganografické zprávy na podezřelém snímku. Nový přístup a výsledky jsou sepsány v této doktorské práci.460 - Katedra informatikyvyhově

Détection statistique d'information cachée dans des images naturelles

La nécessité de communiquer de façon sécurisée n est pas chose nouvelle : depuis l antiquité des méthodes existent afin de dissimuler une communication. La cryptographie a permis de rendre un message inintelligible en le chiffrant, la stéganographie quant à elle permet de dissimuler le fait même qu un message est échangé. Cette thèse s inscrit dans le cadre du projet "Recherche d Informations Cachées" financé par l Agence Nationale de la Recherche, l Université de Technologie de Troyes a travaillé sur la modélisation mathématique d une image naturelle et à la mise en place de détecteurs d informations cachées dans les images. Ce mémoire propose d étudier la stéganalyse dans les images naturelles du point de vue de la décision statistique paramétrique. Dans les images JPEG, un détecteur basé sur la modélisation des coefficients DCT quantifiés est proposé et les calculs des probabilités du détecteur sont établis théoriquement. De plus, une étude du nombre moyen d effondrements apparaissant lors de l insertion avec les algorithmes F3 et F4 est proposée. Enfin, dans le cadre des images non compressées, les tests proposés sont optimaux sous certaines contraintes, une des difficultés surmontées étant le caractère quantifié des donnéesThe need of secure communication is not something new: from ancient, methods exist to conceal communication. Cryptography helped make unintelligible message using encryption, steganography can hide the fact that a message is exchanged.This thesis is part of the project "Hidden Information Research" funded by the National Research Agency, Troyes University of Technology worked on the mathematical modeling of a natural image and creating detectors of hidden information in digital pictures.This thesis proposes to study the steganalysis in natural images in terms of parametric statistical decision. In JPEG images, a detector based on the modeling of quantized DCT coefficients is proposed and calculations of probabilities of the detector are established theoretically. In addition, a study of the number of shrinkage occurring during embedding by F3 and F4 algorithms is proposed. Finally, for the uncompressed images, the proposed tests are optimal under certain constraints, a difficulty overcome is the data quantizationTROYES-SCD-UTT (103872102) / SudocSudocFranceF

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

Side-Information For Steganography Design And Detection

Today, the most secure steganographic schemes for digital images embed secret messages while minimizing a distortion function that describes the local complexity of the content. Distortion functions are heuristically designed to predict the modeling error, or in other words, how difficult it would be to detect a single change to the original image in any given area. This dissertation investigates how both the design and detection of such content-adaptive schemes can be improved with the use of side-information. We distinguish two types of side-information, public and private: Public side-information is available to the sender and at least in part also to anybody else who can observe the communication. Content complexity is a typical example of public side-information. While it is commonly used for steganography, it can also be used for detection. In this work, we propose a modification to the rich-model style feature sets in both spatial and JPEG domain to inform such feature sets of the content complexity. Private side-information is available only to the sender. The previous use of private side-information in steganography was very successful but limited to steganography in JPEG images. Also, the constructions were based on heuristic with little theoretical foundations. This work tries to remedy this deficiency by introducing a scheme that generalizes the previous approach to an arbitrary domain. We also put forward a theoretical investigation of how to incorporate side-information based on a model of images. Third, we propose to use a novel type of side-information in the form of multiple exposures for JPEG steganography

Secure digital documents using Steganography and QR Code

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonWith the increasing use of the Internet several problems have arisen regarding the processing of electronic documents. These include content filtering, content retrieval/search. Moreover, document security has taken a centre stage including copyright protection, broadcast monitoring etc. There is an acute need of an effective tool which can find the identity, location and the time when the document was created so that it can be determined whether or not the contents of the document were tampered with after creation. Owing the sensitivity of the large amounts of data which is processed on a daily basis, verifying the authenticity and integrity of a document is more important now than it ever was. Unsurprisingly document authenticity verification has become the centre of attention in the world of research. Consequently, this research is concerned with creating a tool which deals with the above problem. This research proposes the use of a Quick Response Code as a message carrier for Text Key-print. The Text Key-print is a novel method which employs the basic element of the language (i.e. Characters of the alphabet) in order to achieve authenticity of electronic documents through the transformation of its physical structure into a logical structured relationship. The resultant dimensional matrix is then converted into a binary stream and encapsulated with a serial number or URL inside a Quick response Code (QR code) to form a digital fingerprint mark. For hiding a QR code, two image steganography techniques were developed based upon the spatial and the transform domains. In the spatial domain, three methods were proposed and implemented based on the least significant bit insertion technique and the use of pseudorandom number generator to scatter the message into a set of arbitrary pixels. These methods utilise the three colour channels in the images based on the RGB model based in order to embed one, two or three bits per the eight bit channel which results in three different hiding capacities. The second technique is an adaptive approach in transforming domain where a threshold value is calculated under a predefined location for embedding in order to identify the embedding strength of the embedding technique. The quality of the generated stego images was evaluated using both objective (PSNR) and Subjective (DSCQS) methods to ensure the reliability of our proposed methods. The experimental results revealed that PSNR is not a strong indicator of the perceived stego image quality, but not a bad interpreter also of the actual quality of stego images. Since the visual difference between the cover and the stego image must be absolutely imperceptible to the human visual system, it was logically convenient to ask human observers with different qualifications and experience in the field of image processing to evaluate the perceived quality of the cover and the stego image. Thus, the subjective responses were analysed using statistical measurements to describe the distribution of the scores given by the assessors. Thus, the proposed scheme presents an alternative approach to protect digital documents rather than the traditional techniques of digital signature and watermarking