Exposing image forgery by detecting traces of feather operation

Powerful digital image editing tools make it very easy to produce a perfect image forgery. The feather operation is necessary when tampering an image by copy–paste operation because it can help the boundary of pasted object to blend smoothly and unobtrusively with its surroundings. We propose a blind technique capable of detecting traces of feather operation to expose image forgeries. We model the feather operation, and the pixels of feather region will present similarity in their gradient phase angle and feather radius. An effectual scheme is designed to estimate each feather region pixel׳s gradient phase angle and feather radius, and the pixel׳s similarity to its neighbor pixels is defined and used to distinguish the feathered pixels from un-feathered pixels. The degree of image credibility is defined, and it is more acceptable to evaluate the reality of one image than just using a decision of YES or NO. Results of experiments on several forgeries demonstrate the effectiveness of the technique

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

Detective Narrative and the Problem of Origins in 19th Century England

Working with Fredric Jameson's understanding of genre as a "formal sedimentation" of an ideology, this study investigates the historicity of the detective narrative, what role it plays in bourgeois, capitalist culture, what ways it mediates historical processes, and what knowledge of these processes it preserves. I begin with the problem of the detective narrative's origins. This is a complex and ultimately insoluble problem linked to the limits of historical perspective and compounded by the tendency of genres to erase their own origins. I argue that any critical reading of the detective story beginning with the notion that real crime and working class unrest are the specters that the detective story seeks to exorcise misapprehends the real class struggle that is evidenced in, but also disguised by, the detective story: the struggle between the ascendant (though never assuredly so) bourgeoisie and the receding (though, again, never assuredly so) aristocratic and post-feudal ruling classes. Instead, I argue that it is this class struggle that is apparent in the detective narrative's special structure—the double structure by which it can pose any-origin-whatever as a moment of history and construct that history forward while appearing to uncover it backward. The detective narrative erases precisely the problem of the bourgeoisie's lack of origins (from a feudal perspective) and counterfeits history. For this reason, I locate the detective narrative's beginnings in specific sites where the transfer of power from traditional institutions to bourgeois institutions or institutions reformed by the bourgeoisie, including the Chancery court (in Charles Dickens' Bleak House), the construction of the New Poor Laws of 1834 (in Wilkie Collins' The Dead Secret), and marriage and inheritance in Bleak House and Collins' The Moonstone. Ending with a study of the commonly acknowledged first detective novel, The Moonstone, I conclude that this novel and the generic paradigm of the detective narrative it exemplifies succeed in encrypting the historical discontinuity between post-feudal modes of production and capitalism and that, ultimately, crime is just an alibi for the work of historical reconstruction that the detective narrative carries out

Conventions Were Outraged: Country, House, Fiction

The dissertation traces intersections among subjectivity, gender, desire, and nation in English country house novels from 1921 to 1949. Inter-war and wartime fiction by Daphne du Maurier, Virginia Woolf, Nancy Mitford, P. G. Wodehouse, Elizabeth Bowen, and Evelyn Waugh performs and critiques conventional domestic ideals and, by extension, interrupts the discourses of power that underpin militaristic political certainties. I consider country house novels to be campy endorsements of the English home, in which characters can reimagine, but not escape, their roles within mythologized domestic and national spaces. The Introduction correlates theoretical critiques of nationalism, class, and gender to illuminate continuities among the naïve patriotism of the country house novel and its ironic figurations of rigid class and gender categories. Chapter 1 provides generic and critical contexts through a study of du Maurier’s Rebecca, in which the narrator’s subversion of social hierarchies relies upon the persistence, however ironic, of patriarchal nationalism. That queer desire is the necessary center around which oppressive norms operate only partially mitigates their force. Chapter 2 examines figures of absence in “A Haunted House,” To the Lighthouse, and Orlando. Woolf’s queering of the country house novel relies upon her Gothic figuration of Englishness, in which characters are only included within nationalist spaces by virtue of their exclusion. In Chapter 3, continuities between Orlando and Between the Acts test Woolf’s call to “indifference” to war in Three Guineas. The country house reifies the nostalgic crisis of Woolf’s feminist pacifism: political agency must occupy the borderland between nostalgic idealism and cynical self-abnegation. Chapter 4 examines popular country house novels by Wodehouse, Mitford, Bowen, and Waugh that explicitly engage, with various degrees of seriousness, with political conflicts of the 1930s and ’40s. Exposing disavowed affinities among the country house ethos, English patriotism, and fascist nostalgia provides opportunities to negotiate, if not resolve, ethical quandaries of wartime neutrality, irony, and indifference. By forcing readers to confront their own circumscription by nationalist and gendered expectations, these country house novels ultimately foreclose the possibility of escaping them – but they also demand readers’ renewed commitment to figures of difference and narratives of failure

Columbus State University Honors College: Senior Theses, Fall 2019

This is a collection of senior theses written by honors students at Columbus State University in 2019.https://csuepress.columbusstate.edu/honors_theses/1000/thumbnail.jp

Exposing Image Forgery by Detecting Traces of Feather Operation

Abstract-Powerful digital image editing tools make it very easy to produce a perfect image forgery. The feather operation is necessary when tampering an image by copy-paste operation because it can help the boundary of pasted object to blend smoothly and unobtrusively with its surroundings. We propose a blind technique capable of detecting traces of feather operation to expose image forgeries. We model the feather operation, and the pixels of feather region will present similarity in their gradient phase angle and feather radius. An effectual scheme is designed to estimate each feather region pixel's gradient phase angle and feather radius, and the pixel's similarity to its neighbor pixels is defined and used to distinguish the feathered pixels from unfeathered pixels. The degree of image credibility is defined, and it is more acceptable to evaluate the reality of one image than just using a decision of YES or NO. Results of experiments on several forgeries demonstrate the effectiveness of the technique