An approach to preventing spam using Access Codes with a combination of anti-spam mechanisms

Spam is becoming a more and more severe problem for individuals, networks, organisations and businesses. The losses caused by spam are billions of dollars every year. Research shows that spam contributes more than 80% of e-mails with an increased in its growth rate every year. Spam is not limited to emails; it has started affecting other technologies like VoIP, cellular and traditional telephony, and instant messaging services. None of the approaches (including legislative, collaborative, social awareness and technological) separately or in combination with other approaches, can prevent sufficient of the spam to be deemed a solution to the spam problem. The severity of the spam problem and the limitations of the state-of-the-Art solutions create a strong need for an efficient anti-spam mechanism that can prevent significant volumes of spam without showing any false positives. This can be achieved by an efficient anti-spam mechanism such as the proposed anti-spam mechanism known as "Spam Prevention using Access Codes", SPAC. SPAC targets spam from two angles i.e. to prevent/block spam and to discourage spammers by making the infrastructure environment very unpleasant for them. In addition to the idea of Access Codes, SPAC combines the ideas behind some of the key current technological anti-spam measures to increase effectiveness. The difference in this work is that SPAC uses those ideas effectively and combines them in a unique way which enables SPAC to acquire the good features of a number of technological anti-spam approaches without showing any of the drawbacks of these approaches. Sybil attacks, Dictionary attacks and address spoofing have no impact on the performance of SPAC. In fact SPAC functions in a similar way (i.e. as for unknown persons) for these sorts of attacks. An application known as the "SPAC application" has been developed to test the performance of the SPAC mechanism. The results obtained from various tests on the SPAC application show that SPAC has a clear edge over the existing anti-spam technological approaches

Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach works successfully in a number of practical scenarios, including (but not limited to) observations of light effusions through the windows, on the back wall, or off the victim’s face. My empirical results show that I can successfully confirm hypotheses while capturing short recordings (typically less than 4 minutes long) of the changes in brightness from the victim’s display from a distance of 70 meters. Lastly, for content-based copy detection, I take advantage of a new temporal feature to index a reference library in a manner that is robust to the popular spatial and temporal transformations in pirated videos. My technique narrows the detection gap in the important area of temporal transformations applied by would-be pirates. My large-scale evaluation on real-world data shows that I can successfully detect infringing content from movies and sports clips with 90.0% precision at a 71.1% recall rate, and can achieve that accuracy at an average time expense of merely 5.3 seconds, outperforming the state of the art by an order of magnitude.Doctor of Philosoph

Making Thin Data Thick: User Behavior Analysis with Minimum Information

abstract: With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data. This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick. In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information? In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information. The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Mixing Methods: Practical Insights from the Humanities in the Digital Age

The digital transformation is accompanied by two simultaneous processes: digital humanities challenging the humanities, their theories, methodologies and disciplinary identities, and pushing computer science to get involved in new fields. But how can qualitative and quantitative methods be usefully combined in one research project? What are the theoretical and methodological principles across all disciplinary digital approaches? This volume focusses on driving innovation and conceptualising the humanities in the 21st century. Building on the results of 10 research projects, it serves as a useful tool for designing cutting-edge research that goes beyond conventional strategies

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Congress UPV Proceedings of the 21ST International Conference on Science and Technology Indicators

This is the book of proceedings of the 21st Science and Technology Indicators Conference that took place in València (Spain) from 14th to 16th of September 2016. The conference theme for this year, ‘Peripheries, frontiers and beyond’ aimed to study the development and use of Science, Technology and Innovation indicators in spaces that have not been the focus of current indicator development, for example, in the Global South, or the Social Sciences and Humanities. The exploration to the margins and beyond proposed by the theme has brought to the STI Conference an interesting array of new contributors from a variety of fields and geographies. This year’s conference had a record 382 registered participants from 40 different countries, including 23 European, 9 American, 4 Asia-Pacific, 4 Africa and Near East. About 26% of participants came from outside of Europe. There were also many participants (17%) from organisations outside academia including governments (8%), businesses (5%), foundations (2%) and international organisations (2%). This is particularly important in a field that is practice-oriented. The chapters of the proceedings attest to the breadth of issues discussed. Infrastructure, benchmarking and use of innovation indicators, societal impact and mission oriented-research, mobility and careers, social sciences and the humanities, participation and culture, gender, and altmetrics, among others. We hope that the diversity of this Conference has fostered productive dialogues and synergistic ideas and made a contribution, small as it may be, to the development and use of indicators that, being more inclusive, will foster a more inclusive and fair world