Designing Light Filters to Detect Skin Using a Low-powered Sensor

Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. My research takes a first step in trying to accomplish this long-term goal: In this thesis, I examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), I was able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. The long-term goal to be achieved in the future is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen\u27s mobile device to detect and/or prevent the capture of nude imagery. Thus, I discuss the limitations of this work toward this larger goal, as well as future research directions

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

A system for the visual detection and analysis of obsessive compulsive disorder

Computer vision is a burgeoning field that lends itself to a diverse range of challenging problems. Recent advances in computing power and algorithmic sophistication have prompted a renaissance in the literature of this field, as previously computationally expensive applications have come to the fore. As a result, researchers have begun applying computer vision techniques especially prominently to the analysis of human actions, in an increasingly advanced manner. Chief among the potential applications of such human action analyses are: human surveillance, crowd analysis, gait analysis and health informatics. Even more recently, researchers have begun to realise the potential of computer vision techniques, occasionally in conjunction with other computational approaches, to enhance the quality of life for people living with mental illness. Much of this research has focused on enhancing the existing, traditionally psychiatric, treatment plans for such individuals. Conventionally, these treatment plans have involved a mental health professional taking a face-to-face approach and relying significantly on subjective feedback from the individual, regarding their current condition and progress. However, recent computational methods have focused on augmenting such approaches with objective, e.g. visual, monitoring and feedback on an individual's condition over time. Of these approaches, most have focused on depression, bipolar disorder, dementia, or some form of anxiety. However, none of the approaches described in the literature has been aimed directly at addressing the issues inherent to patients with Obsessive Compulsive Disorder. Motivated by this, the proposed thesis comprises the design and implementation of a system that is capable of detecting and analysing the compulsive behaviours exhibited by individuals with Obsessive Compulsive Disorder. This is accomplished with the aim of assisting mental health professionals in their treatment of such patients. We achieved the aforementioned via a three-pronged approach, which is represented by the three core chapters of this thesis. Firstly, we created a system for the detection of general repetitive (compulsive) behaviours indicative of Obsessive Compulsive Disorder. This was achieved via the use of a combination of optical flow detection and thresholding, an image matching algorithm, and a set of repetition parameters. Via this approach, we achieved good results across a set of three tested videos. Secondly, we proposed a system capable of classifying behaviour as either compulsive or non-compulsive based on the differences in the repetition intensity patterns across a set of behavioural examples. We achieved this via a form of motion history image, which we call a 'Temporal Motion Heat Map' (TMHM). We produced one such heat map per behavioural example and then reduced its dimensionality using histogram-based pixel intensity frequencies, before feeding the result into a Neural Network. This approach achieved a high classification accuracy on the set of 40 tested behavioural examples, thus demonstrating its ability to accurately differentiate between compulsive and non-compulsive behaviours, as compared to a set of existing approaches. Finally, we built a system that is capable of categorising different types of behaviour, both compulsive and non-compulsive, and then assessing them for relative approximate anxiety levels over time. We achieve this using a combination of Speeded-Up Robust Features (SURF) descriptors for behaviour classification and statistical measures for determining the relative anxiety of a given compulsion. This system is also able to achieve a good accuracy when compared with other approaches

Análise de vídeo sensível

Orientadores: Anderson de Rezende Rocha, Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Vídeo sensível pode ser definido como qualquer filme capaz de oferecer ameaças à sua audiência. Representantes típicos incluem ¿ mas não estão limitados a ¿ pornografia, violência, abuso infantil, crueldade contra animais, etc. Hoje em dia, com o papel cada vez mais pervasivo dos dados digitais em nossa vidas, a análise de conteúdo sensível representa uma grande preocupação para representantes da lei, empresas, professores, e pais, devido aos potenciais danos que este tipo de conteúdo pode infligir a menores, estudantes, trabalhadores, etc. Não obstante, o emprego de mediadores humanos, para constantemente analisar grandes quantidades de dados sensíveis, muitas vezes leva a ocorrências de estresse e trauma, o que justifica a busca por análises assistidas por computador. Neste trabalho, nós abordamos este problema em duas frentes. Na primeira, almejamos decidir se um fluxo de vídeo apresenta ou não conteúdo sensível, à qual nos referimos como classificação de vídeo sensível. Na segunda, temos como objetivo encontrar os momentos exatos em que um fluxo começa e termina a exibição de conteúdo sensível, em nível de quadros de vídeo, à qual nos referimos como localização de conteúdo sensível. Para ambos os casos, projetamos e desenvolvemos métodos eficazes e eficientes, com baixo consumo de memória, e adequação à implantação em dispositivos móveis. Neste contexto, nós fornecemos quatro principais contribuições. A primeira é uma nova solução baseada em sacolas de palavras visuais, para a classificação eficiente de vídeos sensíveis, apoiada na análise de fenômenos temporais. A segunda é uma nova solução de fusão multimodal em alto nível semântico, para a localização de conteúdo sensível. A terceira, por sua vez, é um novo detector espaço-temporal de pontos de interesse, e descritor de conteúdo de vídeo. Finalmente, a quarta contribuição diz respeito a uma base de vídeos anotados em nível de quadro, que possui 140 horas de conteúdo pornográfico, e que é a primeira da literatura a ser adequada para a localização de pornografia. Um aspecto relevante das três primeiras contribuições é a sua natureza de generalização, no sentido de poderem ser empregadas ¿ sem modificações no passo a passo ¿ para a detecção de tipos diversos de conteúdos sensíveis, tais como os mencionados anteriormente. Para validação, nós escolhemos pornografia e violência ¿ dois dos tipos mais comuns de material impróprio ¿ como representantes de interesse, de conteúdo sensível. Nestes termos, realizamos experimentos de classificação e de localização, e reportamos resultados para ambos os tipos de conteúdo. As soluções propostas apresentam uma acurácia de 93% em classificação de pornografia, e permitem a correta localização de 91% de conteúdo pornográfico em fluxo de vídeo. Os resultados para violência também são interessantes: com as abordagens apresentadas, nós obtivemos o segundo lugar em uma competição internacional de detecção de cenas violentas. Colocando ambas em perspectiva, nós aprendemos que a detecção de pornografia é mais fácil que a de violência, abrindo várias oportunidades de pesquisa para a comunidade científica. A principal razão para tal diferença está relacionada aos níveis distintos de subjetividade que são inerentes a cada conceito. Enquanto pornografia é em geral mais explícita, violência apresenta um espectro mais amplo de possíveis manifestaçõesAbstract: Sensitive video can be defined as any motion picture that may pose threats to its audience. Typical representatives include ¿ but are not limited to ¿ pornography, violence, child abuse, cruelty to animals, etc. Nowadays, with the ever more pervasive role of digital data in our lives, sensitive-content analysis represents a major concern to law enforcers, companies, tutors, and parents, due to the potential harm of such contents over minors, students, workers, etc. Notwithstanding, the employment of human mediators for constantly analyzing huge troves of sensitive data often leads to stress and trauma, justifying the search for computer-aided analysis. In this work, we tackle this problem in two ways. In the first one, we aim at deciding whether or not a video stream presents sensitive content, which we refer to as sensitive-video classification. In the second one, we aim at finding the exact moments a stream starts and ends displaying sensitive content, at frame level, which we refer to as sensitive-content localization. For both cases, we aim at designing and developing effective and efficient methods, with low memory footprint and suitable for deployment on mobile devices. In this vein, we provide four major contributions. The first one is a novel Bag-of-Visual-Words-based pipeline for efficient time-aware sensitive-video classification. The second is a novel high-level multimodal fusion pipeline for sensitive-content localization. The third, in turn, is a novel space-temporal video interest point detector and video content descriptor. Finally, the fourth contribution comprises a frame-level annotated 140-hour pornographic video dataset, which is the first one in the literature that is appropriate for pornography localization. An important aspect of the first three contributions is their generalization nature, in the sense that they can be employed ¿ without step modifications ¿ to the detection of diverse sensitive content types, such as the previously mentioned ones. For validation, we choose pornography and violence ¿ two of the commonest types of inappropriate material ¿ as target representatives of sensitive content. We therefore perform classification and localization experiments, and report results for both types of content. The proposed solutions present an accuracy of 93% in pornography classification, and allow the correct localization of 91% of pornographic content within a video stream. The results for violence are also compelling: with the proposed approaches, we reached second place in an international competition of violent scenes detection. Putting both in perspective, we learned that pornography detection is easier than its violence counterpart, opening several opportunities for additional investigations by the research community. The main reason for such difference is related to the distinct levels of subjectivity that are inherent to each concept. While pornography is usually more explicit, violence presents a broader spectrum of possible manifestationsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1572763, 1197473CAPE

Activity related biometrics for person authentication

One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods. The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics. Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features. Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space. Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements. The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance. The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Décoder l’habileté perceptive dans le cerveau humain : contenu représentationnel et computations cérébrales

La capacité à reconnaître les visages de nos collègues, de nos amis et de nos proches est essentielle à notre réussite en tant qu'êtres sociaux. Notre cerveau accomplit cet exploit facilement et rapidement, dans une série d’opérations se déroulant en quelques dizaines de millisecondes à travers un vaste réseau cérébral du système visuel ventral. L’habileté à reconnaître les visages, par contre, varie considérablement d’une personne à l’autre. Certains individus, appelés «super-recognisers», sont capables de reconnaître des visages vus une seule fois dans la rue des années plus tôt. D’autres, appelés «prosopagnosiques», sont incapables de reconnaître le visage de leurs collègues ou leurs proches, même avec une vision parfaite. Une question simple reste encore largement sans réponse : quels mécanismes expliquent que certains individus sont meilleurs à reconnaître des visages? Cette thèse rapporte cinq articles étudiant les mécanismes perceptifs (articles 1, 2, 3) et cérébraux (articles 4, 5) derrière ces variations à travers différentes populations d’individus. L’article 1 décrit le contenu des représentations visuelles faciales chez une population avec un diagnostic de schizophrénie et d’anxiété sociale à l’aide d’une technique psychophysique Bubbles. Nous révélons pour la première fois les mécanismes en reconnaissance des expressions de cette population: un déficit de reconnaissance est accompagné par i) une sous-utilisation de la région des yeux des visages expressifs et ii) une sous-utilisation des détails fins. L’article 2 valide ensuite une nouvelle technique permettant de révéler simultanément le contenu visuel dans trois dimensions psychophysiques centrales pour le système visuel — la position, les fréquences spatiales, et l’orientation. L’article 3 a mesuré, à l'aide de cette nouvelle technique, le contenu représentationnel de 120 individus pendant la discrimination faciale du sexe et des expressions ( >500,000 observations). Nous avons observé de fortes corrélations entre l’habileté à discriminer le sexe et les expressions des visages, ainsi qu'entre l’habileté à discriminer le sexe et l’identité. Crucialement, plus un individu est habile en reconnaissance faciale, plus il utilise un contenu représentationnel similaire entre les tâches. L’article 4 a examiné les computations cérébrales de super-recognisers en utilisant l’électroencéphalographie haute-densité (EEG) et l’apprentissage automatique. Ces outils ont permis de décoder, pour la première fois, l’habileté en reconnaissance faciale à partir du cerveau avec jusqu’à 80% d’exactitude –– et ce à partir d’une seule seconde d’activité cérébrale. Nous avons ensuite utilisé la Representational Similarity Analysis (RSA) pour comparer les représentations cérébrales de nos participants à celles de modèles d’apprentissage profond visuels et langagiers. Les super-recognisers, comparé aux individus avec une habileté typique, ont des représentations cérébrales plus similaires aux computations visuelles et sémantiques de ces modèles optimaux. L’article 5 rapporte une investigation des computations cérébrales chez le cas le plus spécifique et documenté de prosopagnosie acquise, la patiente PS. Les mêmes outils computationnels et d’imagerie que ceux de l’article 4 ont permis i) de décoder les déficits d’identification faciale de PS à partir de son activité cérébrale EEG, et ii) de montrer pour la première fois que la prosopagnosie est associée à un déficit des computations visuelles de haut niveau et des computations cérébrales sémantiques.The ability to recognise the faces of our colleagues, friends, and family members is critical to our success as social beings. Our brains accomplish this feat with astonishing ease and speed, in a series of operations taking place in tens of milliseconds across a vast brain network of the visual system. The ability to recognise faces, however, varies considerably from one person to another. Some individuals, called "super-recognisers", are able to recognise faces seen only once years earlier. Others, called "prosopagnosics", are unable to recognise the faces of their colleagues or relatives, even with perfect vision and typical intelligence. A simple question remains largely unanswered: what mechanisms explain why some individuals are better at recognizing faces? This thesis reports five articles studying the perceptual (article 1, 2, 3) and neural (article 4, 5) mechanisms behind these variations across different populations of individuals. Article 1 describes the content of visual representations of faces in a population with a comorbid diagnosis of schizophrenia and social anxiety disorder using an established psychophysical technique, Bubbles. We reveal for the first time the perceptual mechanisms of expression recognition in this population: a recognition deficit is accompanied by i) an underutilization of the eye region of expressive faces and ii) an underutilization of fine details. Article 2 then validates a new psychophysical technique that simultaneously reveals the visual content in three dimensions central to the visual system — position, spatial frequencies, and orientation. We do not know, however, whether skilled individuals perform well across a variety of facial recognition tasks and, if so, how they accomplish this feat. Article 3 measured, using the technique validated in article 2, the perceptual representations of 120 individuals during facial discrimination of gender and expressions (total of >500,000 trials). We observed strong correlations between the ability to discriminate gender and facial expressions, as well as between the ability to discriminate gender and identify faces. More importantly, we found a positive correlation between individual ability and the similarity of perceptual representations used across these tasks. Article 4 examined differences in brain dynamics between super-recognizers and typical individuals using high-density electroencephalography (EEG) and machine learning. These tools allowed us to decode, for the first time, facial recognition ability from the brain with up to 80% accuracy — using a mere second of brain activity. We then used Representational Similarity Analysis (RSA) to compare our participants' brain representations to those of deep learning models of object and language classification. This showed that super-recognisers, compared to individuals with typical perceptual abilites, had brain representations more similar to the visual and semantic computations of these optimal models. Article 5 reports an investigation of brain computations in the most specific and documented case of acquired prosopagnosia, patient PS. The same computational tools used in article 4 enabled us to decode PS's facial identification deficits from her brain dynamics. Crucially, associations between brain deep learning models showed for the first time that prosopagnosia is associated with deficits in high-level visual and semantic brain computations

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

Gravitational Imagination: Picturing Suspension From Eadweard Muybridge To The Space Age

Resisting gravity holds an allure. Situating that appeal within the realm of art history, my dissertation charts modern aesthetic efforts to channel and challenge gravitational force—casting suspension as vital to modernism. I contend that new modes of pictorial time—and, in turn, novel possibilities for embodied engagement—emerged once photographic technology accelerated enough to catch airborne bodies and hold them aloft in the space of an image—documenting a potential which was actualized in the Space Age, when humans first experienced sustained weightlessness. Tracing an ungrounded sensibility that emerged between these nodal points, my project offers a thematic account of how gravitational disruption coheres in pictorial composition and perceptual effects. Drawing upon a range of interdisciplinary sources and period voices, my chapters posit the rise of a form of suspended viewership—which does not presume grounded-ness or fixed coordinates, either within artworks or on our part. From Eadweard Muybridge’s photographs of figures held in momentary flight to artists such as Helen Frankenthaler and Marcel Duchamp enacting an “aerial gesture” that employs and subverts gravity, and from Claude Monet’s “upside down” waterlily paintings to Aaron Siskind’s levitational midcentury imagery, my case studies explore increasingly unbound aesthetic terrain. Once gravity became dislodged in visual representation, I argue, formal axes were opened to more symbolic creative dimensions. With that metaphoric tenor, this dissertation defines a pictorial suspension ripe with potential—and charged with the power to resist seemingly inexorable forces. Materializing a stillness that arose in the face of modern momentum, the objects at its core open space for a “gravitational imagination”—founded in the world but also challenging its limits