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

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

Progressive Randomization For Steganalysis

In this paper, we describe a new methodology to detect the presence of hidden digital content in the Least Significant Bits (LSB) of images. We introduce the Progressive Randomization (PR) technique that captures statistical artifacts inserted during the hiding process. Our technique is a progressive application of LSB modifying transformations that receives an image as input, and returns n images that only differ in the LSB from the initial image. Each step of the progressive randomization approach represents a possible content-hiding scenario with increasing size, and increasing LSB entropy. We validate our method with 20,000 real, non-synthetic images. Using only statistical descriptors of LSB occurrences, our method already performs as well or better than comparable techniques in the literature. © 2006 IEEE.314319Anderson, R., Petitcolas, F., On the limits of steganography (1998) IEEE Journal of Selected Areas in Communications, 16, pp. 474-481. , mayHart, S.V., Forensic examination of digital evidence: A guide for law enforcement (2004) NIJ-USMorris, S., The future of netcrime now (1) - threats and challenges (2004), Home Office Crime and Policing Group, Tech. Rep. 62/04Mercuri, R.T., The many colors of multimedia security (2004) Communications of the ACM, 47, pp. 25-29Friedman, J., Hastie, T., Tibshirani, R., (2001) The elements of statistical learning, , Springer VerlagJohnson, N., Jajodia, S., Steganalysis of images created using current steganography software (1998) Proc. of the 2nd Intl. Workshop on Information Hiding, pp. 273-289. , Springer-VerlagWayner, P., (2002) Disappearing cryptography, , Morgan Kaufmann PubPetitcolas, F., Anderson, R., Kuhn, M., Information hiding - A survey (1999) Proc. of the IEEE, 87, pp. 1062-1078Johnson, N., Jajodia, S., Exploring steganography: Seeing the unseen (1998) IEEE Computer, 31 (2), pp. 26-34Westfeld, A., Pfitzmann, A., Attacks on steganographic systems (1999) Proc. of the 3rd Intl. Workshop on Information Hiding, pp. 61-76Fridrich, J., Du, R., Long, M., Steganalysis of LSB enconding in color images (2000) ICME - Intl. Conf. Multimedia Expo, 3, pp. 1279-1282. , AugFridrich, J., Goljan, M., Du, R., Reliable detection of LSB steganography in color and grayscale images (2001) Proc. of the ACM Workshop on Multimedia and Security, pp. 27-30Lyu, S., Farid, H., Detecting hidden messages using higher-order statistics and support vector machines (2002) Proc. of the 5th Intl. Workshop on Information Hiding, pp. 340-354Farid, H., Detecting hidden messages using higher-order statistical models (2002) Proc. of the 5th Intl. Conf on Image Processing, 2, pp. 905-908Vaidyanathan, P., Quadrature mirror filter banks, m-band extensions and perfect reconstruction techniques (1987) IEEE ASSP Magazine, pp. 4-20. , julShi, Y.Q., Image Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network (2005) ICME - Intl. Conf. Multimedia Expo, pp. 268-272. , JulProvos, N., Honeyman, P., (2001) Detecting steganographic content on the internet, , University of Michigan, Tech. Rep. CITI 01-1aMaurer, U., A universal statistical test for random bit generators (1992) Journal of Cryptology, 5, pp. 89-105Harris, C., Stephens, M., A combined corner and edge detector (1988) Proc. of The 4th Alvey Vision Conf, pp. 147-151Cristianini, N., Shawe-Taylor, J., (2000) An Introduction to Support Vector Machines, , Cambridge U. PressVenables, W.N., Smith, D.M., An introduction to R: A programming environment for data analysis and graphics (2005) R Development Core Tea

Progressive randomization for steganalysis

Orientador: Siome Klein GoldensteinDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho, nós descrevemos uma nova metodologia para detectar a presença de conteúdo digital escondido nos bits menos significativos (LSBs) de imagens. Nós introduzimos a técnica de Randomização Progressiva (PR), que captura os artefatos estatísticos inseridos durante um processo de mascaramento com aleatoriedade espacial. Nossa metodologia consiste na progressiva aplicação de transformações de mascaramento nos LSBs de uma imagem. Ao receber uma imagem I como entrada, o método cria n imagens, que apenas se diferenciam da imagem original no canal LSB. Cada estágio da Randomização Progressiva representa possíveis processos de mascaramento com mensagens de tamanhos diferentes e crescente entropia no canal LSB. Analisando esses estágios, nosso arcabouço de detecção faz a inferência sobre a presença ou não de uma mensagem escondida na imagem I. Nós validamos nossa metodologia em um banco de dados com 20.000 imagens reais. Nosso método utiliza apenas descritores estatísticos dos LSBs e já apresenta melhor qualidade de classificação que os métodos comparáveis descritos na literaturaAbstract: In this work, we describe a new methodology to detect the presence of hidden digital content in the Least Significant Bits (LSBs) of images. We introduce the Progressive Randomization technique that captures statistical artifacts inserted during the hiding process. Our technique is a progressive application of LSB modifying transformations that receives an image as input, and produces n images that only differ in the LSB from the initial image. Each step of the progressive randomization approach represents a possible content-hiding scenario with increasing size, and increasing LSB entropy. Analyzing these steps, our detection framework infers whether or not the input image I contains a hidden message. We validate our method with 20,000 real, non-synthetic images. Our method only uses statistical descriptors of LSB occurrences and already performs better than comparable techniques in the literatureMestradoVisão ComputacionalMestre em Ciência da Computaçã

The Unseen Challenge Data Sets

Nowadays, it is paramount to study and develop robust algorithms to detect the very existence of hidden messages in digital images. In this paper, we provide two data sets for the Unseen Challenge of the First IEEE Workitorial on Vision of the Unseen (WVU). Example usage of the data sets is demonstrated with the stegdetect analysis tool, with surprising results reported. Our objective is to challenge researchers to assess their Digital Image Steganalysis state-of-the-art algorithms. © 2008 IEEE.Petitcolas, F., Anderson, R., Kuhn, M., Information hiding - a survey (1999) Proceedings of the IEEE, 87 (7), pp. 1062-1078. , JulyS. V. Hart, Forensic examination of digital evidence: a guide for law enforcement, National Institute of Justice NIJ-US, Washington DC, USA, Tech. Rep. NCJ 199408, September 2004Morris, S., (2004) The future of netcrime now: Part 1 - threats and challenges, , Home Office Crime and Policing Group, Washington DC, USA, Tech. Rep. 62/04Rocha, A., Goldenstein, S., Steganography and steganalysis in digital multimedia: Hype or hallelujah? (2007) Journal of Theoretical and Applied Computing (RITA), 14 (2)Norman, B., (1980) Secret warfare, the battle of Codes and Ciphers, , lst ed. Acropolis BooksKahn, D., The history of steganography (1996) Intl. Workshop in Information Hiding (IHW), pp. 1-5Wayner, P., (2002) Disappearing Cryptography - Information Hiding: Steganography & Watermarking, , 2nd ed. Morgan KaufmannWallich, P., Getting the message (2003) IEEE Spectrum, 40 (4), pp. 38-40. , AprilCass, S., Listening in (2003) IEEE Spectrum, 40 (4), pp. 32-37. , AprilKumagai, J., Mission impossible? (2003) IEEE Spectrum, 40 (4), pp. 26-31. , AprilProvos, N., Honeyman, P., Hide and seek: An introduction to steganography (2003) IEEE Security & Privacy Magazine, 1 (3), pp. 32-44. , MarchAnderson, R., Petitcolas, F., On the limits of steganography (1998) Journal of Selected Areas in Communications (JSAC), 16 (4), pp. 474-481. , MayRocha, A., Goldenstein, S., Progressive randomization for steganalysis (2006) Intl. Workshop on Multimedia and Signal Processing (MMSP), pp. 314-319Gonzalez, R., Woods, R., (2007) Digital Image Processing, , 3rd ed. Prentice-HallRocha, A., Goldenstein, S., Costa, H.A.X., Chaves, L.M., Camaleão: Um software de esteganografia para proteção e seguranca digital (2004) Simpósio de Segurança em Informática (SSI)A. Westfeld, F5 - a steganographic algorithm: High capacity despite better steganalysis, in Intl. Workshop in Information Hiding (IHW), 2001, pp. 289?-302Westfeld, A., Pfitzmann, A., Attacks on steganographic systems (1999) Intl. Workshop in Information Hiding (IHW), pp. 61-76Provos, N., Defending against statistical steganalysis (2001) Usenix Security Symposium, 10, pp. 24-3

Pr: More Than Meets The Eye

In this paper, we introduce a new image descriptor for broad Image Categorization, the Progressive Randomization (PR), that uses perturbations on the values of the Least Significant Bits (LSB) of images. We show that different classes of images have a distinct behavior under our methodology, and that using statistical descriptors of LSB occurrences and enough training examples, the method already performs as well or better than comparable existing techniques in the literature. With few training examples, PR still has good separability, and its accuracy increases with the size of the training set. We validate our method using four image databases with different categories. ©2007 IEEE.Allwein, E.L., Schapire, R.E., Singer, Y., Reducing multiclass to binary: A unifying approach for margin classifiers (2001) JMLR, 1, pp. 113-141Bosch, A., Zisserman, A., Munoz, X., Scene classification via pLSA (2006) ECCVCutzu, F., Hammoud, R., Leykin, A., Distinguishing paintings from photographs (2005) CVIU, 100, pp. 249-273Fei, L.F., Fergus, R., Perona, P., One-shot learning of object categories (2006) TPAMI, 28 (4), pp. 594-611Freedman, D., Pisani, R., Purves, R., Statistics (1978) George J, , McLeod LimitedFridrich, J., Goljan, M., Du, R., Reliable detection of LSB steganography in color and grayscale images (2001) ACM Multimedia and Security, pp. 27-30Friedman, J., Hastie, T., Tibshirani, R., (2001) The elements of statistical learning, , Springer VerlagGrauman, K., Darrell, T., Efficient Image Matching with Distributions of Local Invariant Features (2005) CVPR, pp. 627-634Heidemann, G., Unsupervised image categorization (2005) Image and Vision Computing, 23, pp. 861-876J. Sivic and B. Russell and A. Efros and A. Zisserman and and W. Freeman. Discovering objects and their location in images. In ICCV, pages 370-377, 2005Luo, J., Savakis, A., Indoor vs. outdoor classification of consumer photographs using low-level and semantic features (2001) ICIP, pp. 745-748Lyu, S., Farid, H., How realistic is photorealistic? (2005) IEEE Trans. on Signal Proc, 53, pp. 845-850Marszałek, M., Schmid, C., Spatial Weighting for Bag-of-Features (2006) CVPR, pp. 2118-2125Maurer, U., A universal statistical test for random bit generators (1992) Journal of Cryptology, 5, pp. 89-105Oliva, A., Torralba, A.B., Modeling the shape of the scene: A holistic representation of the spatial envelope (2001) IJCV, 42 (3), pp. 145-175Payne, A., Singh, S., Indoor vs. outdoor scene classification in digital photographs (2005) Pattern Recognition, 38 (10), pp. 1533-1545A. Rocha and S. Goldenstein. Progressive Randomization for Steganalysis. In 8th IEEE Intl. MMSP, 2006Serrano, N., Savakis, A., Luo, J., A computationally efficient approach to indoor/outdoor scene classification (2002) 16th ICPR, pp. 146-149Vailaya, A., Jain, A., Zhang, H.J., On image classification: City images vs. landscapes (1998) Pattern Recognition, 31, pp. 1921-1935Vogel, J., Schiele, B., A semantic typicality measure for natural scene categorization (2004) DAGM Annual Pattern Recognition SymposiumWayner, P., (2002) Disappearing cryptography, , Morgan Kaufmann PublishersWestfeld, A., Pfitzmann, A., Attacks on steganographic systems (1999) 3rd Intl. Workshop on Information Hiding, pp. 61-7