Pornographic Image Recognition via Weighted Multiple Instance Learning

In the era of Internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. However, this task is very challenging as the key pornographic contents (e.g., breast and private part) in an image often lie in local regions of small size. In this paper, we model each image as a bag of regions, and follow a multiple instance learning (MIL) approach to train a generic region-based recognition model. Specifically, we take into account the region's degree of pornography, and make three main contributions. First, we show that based on very few annotations of the key pornographic contents in a training image, we can generate a bag of properly sized regions, among which the potential positive regions usually contain useful contexts that can aid recognition. Second, we present a simple quantitative measure of a region's degree of pornography, which can be used to weigh the importance of different regions in a positive image. Third, we formulate the recognition task as a weighted MIL problem under the convolutional neural network framework, with a bag probability function introduced to combine the importance of different regions. Experiments on our newly collected large scale dataset demonstrate the effectiveness of the proposed method, achieving an accuracy with 97.52% true positive rate at 1% false positive rate, tested on 100K pornographic images and 100K normal images.Comment: 9 pages, 3 figure

A Path Analysis of Factors Affecting Social Control of Cybercultural Transgressions

Social control of cyberspace is a necessity to restrict online transgressions (non-normative behaviors), and reduce their disruptive effects. The current study aimed at examining the factors affecting social control of cybercultural transgressions. A questionnaire was administered to Iranian social media users, and 989 participants have filled it out. A path analysis model was constructed testing the effects of Low Self-Control, Depression, Negative Interpersonal Relationships, Computer/ Internet Self-Efficacy, Netiquette, and Normative Beliefs on Transgressive Behaviors, and Transgressive Content Consumption. The results showed that Low Self-Control increased both criterion variables, and fully or partially mediated the effects of other variables on them, except for Negative Interpersonal Relationships. The important contribution of the current study was the recognition of the role of self-control as a mediator among examined variables. The findings of this study can be employed to devise new policies and initiatives to socially control the cybercultural transgressions, without applying coercion

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Detecção de pornografia em vídeos através de técnicas de aprendizado profundo e informações de movimento

Orientadores: Anderson de Rezende Rocha, Vanessa TestoniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Com o crescimento exponencial de gravações em vídeos disponíveis online, a moderação manual de conteúdos sensíveis, e.g, pornografia, violência e multidões, se tornou impra- ticável, aumentando a necessidade de uma filtragem automatizada. Nesta linha, muitos trabalhos exploraram o problema de detecção de pornografia, usando abordagens que vão desde a detecção de pele e nudez, até o uso de características locais e sacola de pala- vras visuais. Contudo, essas técnicas sofrem com casos ambíguos (e.g., cenas em praia, luta livre), produzindo muitos falsos positivos. Isto está possivelmente relacionado com o fato de que essas abordagens estão desatualizadas, e de que poucos autores usaram a informação de movimento presente nos vídeos, que pode ser crucial para a desambi- guação visual dos casos mencionados. Indo adiante para superar estas questões, neste trabalho, nós exploramos soluções de aprendizado em profundidade para o problema de detecção de pornografia em vídeos, levando em consideração tanto a informação está- tica, quanto a informação de movimento disponível em cada vídeo em questão. Quando combinamos as características estáticas e de movimento, o método proposto supera as soluções existentes na literatura. Apesar de as abordagens de aprendizado em profun- didade, mais especificamente as Redes Neurais Convolucionais (RNC), terem alcançado resultados impressionantes em outros problemas de visão computacional, este método tão promissor ainda não foi explorado suficientemente no problema detecção de pornografia, principalmente no que tange à incorporação de informações de movimento presente no vídeo. Adicionalmente, propomos novas formas de combinar as informações estáticas e de movimento usando RNCs, que ainda não foram exploradas para detecção de pornografia, nem em outras tarefas de reconhecimento de ações. Mais especificamente, nós exploramos duas fontes distintas de informação de movimento: Campos de deslocamento de Fluxo Óptico, que tem sido tradicionalmente usados para classificação de vídeos; e Vetores de Movimento MPEG. Embora Vetores de Movimento já tenham sido utilizados pela litera- tura na tarefa de detecção de pornografia, neste trabalho nós os adaptamos, criando uma representação visual apropriada, antes de passá-los a uma rede neural convolucional para aprendizado e extração de características. Nossos experimentos mostraram que, apesar de a técnica de Vetores de Movimento MPEG possuir uma performance inferior quando utilizada de forma isolada, quando comparada à técnica baseada em Fluxo Óptico, ela consegue uma performance similar ao complementar a informação estática, com a van- tagem de estar presente, por construção, nos vídeos, enquanto se decodifica os frames, evitando a necessidade da computação mais cara do Fluxo Óptico. Nossa melhor aborda- gem proposta supera os métodos existentes na literatura em diferentes datasets. Para o dataset Pornography 800, o método consegue uma acurácia de classificação de 97,9%, uma redução do erro de 64,4% quando comparado com o estado da arte (94,1% de acu- rácia neste dataset). Quando consideramos o dataset Pornography 2k, mais desafiador, nosso melhor método consegue um acurácia de 96,4%, reduzindo o erro de classificação em 14,3% em comparação ao estado da arte (95,8%)Abstract: With the exponential growth of video footage available online, human manual moderation of sensitive scenes, e.g., pornography, violence and crowd, became infeasible, increasing the necessity for automated filtering. In this vein, a great number of works has explored the pornographic detection problem, using approaches ranging from skin and nudity de- tection, to local features and bag of visual words. Yet, these techniques suffer from some ambiguous cases (e.g., beach scenes, wrestling), producing too much false positives. This is possibly related to the fact that these approaches are somewhat outdated, and that few authors have used the motion information present in videos, which could be crucial for the visual disambiguation of these cases. Setting forth to overcome these issues, in this work, we explore deep learning solutions to the problem of pornography detection in videos, tak- ing into account both the static and the motion information available for each questioned video. When incorporating the static and motion complementary features, the proposed method outperforms the existing solutions in the literature. Although Deep Learning ap- proaches, more specifically Convolutional Neural Networks (CNNs), have achieved striking results on other vision-related problems, such promising methods are still not sufficiently explored in pornography detection while incorporating motion information. We also pro- pose novel ways for combining the static and the motion information using CNNs, that have not been explored in pornography detection, nor in other action recognition tasks before. More specifically, we explore two distinct sources of motion information herein: Optical Flow displacement fields, which have been traditionally used for video classifica- tion; and MPEG Motion Vectors. Although Motion Vectors have already been used for pornography detection tasks in the literature, in this work, we adapt them, by finding an appropriate visual representation, before feeding a convolution neural network for feature learning and extraction. Our experiments show that although the MPEG Motion Vectors technique has an inferior performance by itself, than when using its Optical Flow coun- terpart, it yields a similar performance when complementing the static information, with the advantage of being present, by construction, in the video while decoding the frames, avoiding the need for the more expensive Optical Flow calculations. Our best approach outperforms existing methods in the literature when considering different datasets. For the Pornography 800 dataset, it yields a classification accuracy of 97.9%, an error re- duction of 64.4% when compared to the state of the art (94.1% in this dataset). Finally, considering the more challenging Pornography 2k dataset, our best method yields a clas- sification accuracy of 96.4%, reducing the classification error in 14.3% when compared to the state of the art (95.8% in the same dataset)MestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoFuncampCAPE

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

The Democratization of News - Analysis and Behavior Modeling of Users in the Context of Online News Consumption

Die Erfindung des Internets ebnete den Weg für die Demokratisierung von Information. Die Tatsache, dass Nachrichten für die breite Öffentlichkeit zugänglicher wurden, barg wichtige politische Versprechen, wie zum Beispiel das Erreichen von zuvor uninformierten und daher oft inaktiven Bürgern. Diese konnten sich nun dank des Internets tagesaktuell über das politische Geschehen informieren und selbst politisch engagieren. Während viele Politiker und Journalisten ein Jahrzehnt lang mit dieser Entwicklung zufrieden waren, änderte sich die Situation mit dem Aufkommen der sozialen Online-Netzwerke (OSN). Diese OSNs sind heute nahezu allgegenwärtig – so beziehen inzwischen $67\%$ der Amerikaner zumindest einen Teil ihrer Nachrichten über die sozialen Medien. Dieser Trend hat die Kosten für die Veröffentlichung von Inhalten weiter gesenkt. Dies sah zunächst nach einer positiven Entwicklung aus, stellt inzwischen jedoch ein ernsthaftes Problem für Demokratien dar. Anstatt dass eine schier unendliche Menge an leicht zugänglichen Informationen uns klüger machen, wird die Menge an Inhalten zu einer Belastung. Eine ausgewogene Nachrichtenauswahl muss einer Flut an Beiträgen und Themen weichen, die durch das digitale soziale Umfeld des Nutzers gefiltert werden. Dies fördert die politische Polarisierung und ideologische Segregation. Mehr als die Hälfte der OSN-Nutzer trauen zudem den Nachrichten, die sie lesen, nicht mehr ($54\%$ machen sich Sorgen wegen Falschnachrichten). In dieses Bild passt, dass Studien berichten, dass Nutzer von OSNs dem Populismus extrem linker und rechter politischer Akteure stärker ausgesetzt sind, als Personen ohne Zugang zu sozialen Medien. Um die negativen Effekt dieser Entwicklung abzumildern, trägt meine Arbeit zum einen zum Verständnis des Problems bei und befasst sich mit Grundlagenforschung im Bereich der Verhaltensmodellierung. Abschließend beschäftigen wir uns mit der Gefahr der Beeinflussung der Internetnutzer durch soziale Bots und präsentieren eine auf Verhaltensmodellierung basierende Lösung. Zum besseren Verständnis des Nachrichtenkonsums deutschsprachiger Nutzer in OSNs, haben wir deren Verhalten auf Twitter analysiert und die Reaktionen auf kontroverse - teils verfassungsfeindliche - und nicht kontroverse Inhalte verglichen. Zusätzlich untersuchten wir die Existenz von Echokammern und ähnlichen Phänomenen. Hinsichtlich des Nutzerverhaltens haben wir uns auf Netzwerke konzentriert, die ein komplexeres Nutzerverhalten zulassen. Wir entwickelten probabilistische Verhaltensmodellierungslösungen für das Clustering und die Segmentierung von Zeitserien. Neben den Beiträgen zum Verständnis des Problems haben wir Lösungen zur Erkennung automatisierter Konten entwickelt. Diese Bots nehmen eine wichtige Rolle in der frühen Phase der Verbreitung von Fake News ein. Unser Expertenmodell - basierend auf aktuellen Deep-Learning-Lösungen - identifiziert, z. B., automatisierte Accounts anhand ihres Verhaltens. Meine Arbeit sensibilisiert für diese negative Entwicklung und befasst sich mit der Grundlagenforschung im Bereich der Verhaltensmodellierung. Auch wird auf die Gefahr der Beeinflussung durch soziale Bots eingegangen und eine auf Verhaltensmodellierung basierende Lösung präsentiert