140 research outputs found

    Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network

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    Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV -CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper

    Conventional and Neural Architectures for Biometric Presentation Attack Detection

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    Facial biometrics, which enable an efficient and reliable method of person recognition, have been growing continuously as an active sub-area of computer vision. Automatic face recognition offers a natural and non-intrusive method for recognising users from their facial characteristics. However, facial recognition systems are vulnerable to presentation attacks (or spoofing attacks) when an attacker attempts to hide their true identity and masquerades as a valid user by misleading the biometric system. Thus, Facial Presentation Attack Detection (Facial PAD) (or facial antispoofing) techniques that aim to protect face recognition systems from such attacks, have been attracting more research attention in recent years. Various systems and algorithms have been proposed and evaluated. This thesis explores and compares some novel directions for detecting facial presentation attacks, including traditional features as well as approaches based on deep learning. In particular, different features encapsulating temporal information are developed and explored for describing the dynamic characteristics in presentation attacks. Hand-crafted features, deep neural architectures and their possible extensions are explored for their application in PAD. The proposed novel traditional features address the problem of modelling distinct representations of presentation attacks in the temporal domain and consider two possible branches: behaviour-level and texture-level temporal information. The behaviour-level feature is developed from a symbolic system that was widely used in psychological studies and automated emotion analysis. Other proposed traditional features aim to capture the distinct differences in image quality, shadings and skin reflections by using dynamic texture descriptors. This thesis then explores deep learning approaches using different pre-trained neural architectures with the aim of improving detection performance. In doing so, this thesis also explores visualisations of the internal representation of the networks to inform the further development of such approaches for improving performance and suggest possible new directions for future research. These directions include interpretable capability of deep learning approaches for PAD and a fully automatic system design capability in which the network architecture and parameters are determined by the available data. The interpretable capability can produce justifications for PAD decisions through both natural language and saliency map formats. Such systems can lead to further performance improvement through the use of an attention sub-network by learning from the justifications. Designing optimum deep neural architectures for PAD is still a complex problem that requires substantial effort from human experts. For this reason, the necessity of producing a system that can automatically design the neural architecture for a particular task is clear. A gradient-based neural architecture search algorithm is explored and extended through the development of different optimisation functions for designing the neural architectures for PAD automatically. These possible extensions of the deep learning approaches for PAD were evaluated using challenging benchmark datasets and the potential of the proposed approaches were demonstrated by comparing with the state-of-the-art techniques and published results. The proposed methods were evaluated and analysed using publicly available datasets. Results from the experiments demonstrate the usefulness of temporal information and the potential benefits of applying deep learning techniques for presentation attack detection. In particular, the use of explanations for improving usability and performance of deep learning PAD techniques and automatic techniques for the design of PAD neural architectures show considerable promise for future development

    Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

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    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

    Authorization and authentication strategy for mobile highly constrained edge devices

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    The rising popularity of mobile devices has driven the need for faster connection speeds and more flexible authentication and authorization methods. This project aims to develop and implement an innovative system that provides authentication and authorization for both the device and the user. It also facilitates real-time user re-authentication within the application, ensuring transparency throughout the process. Additionally, the system aims to establish a secure architecture that minimizes the computational requirements on the client's device, thus optimizing the device's battery life. The achieved results have demonstrated satisfactory outcomes, validating the effectiveness of the proposed solution. However, there is still potential for further improvement to enhance its overall performance

    Trust and Voice Biometric Authentication: Understanding the Levels of User’s Trust on Authentication Methods

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    Due to the singularity of the distinct biometric traits, biometric authentication factors have become increasingly prevalent in daily life and are predicted to target future authentication methods. Previous studies established that the human voice is one of the most natural, non-intrusive, and convenient behavioral biometric factors compared to other biometric authentication methods. Despite the non-intrusive characteristics of voice biometric authentication, it has been brought under scrutiny for many reasons, including the accuracy of biometric data, a general societal trust and distrust with technology and the risk of theft and imitation. Although, when it comes to trusting technology, users’ perceptions change with time through continued use of technology, and thus allowing perceptions and opinions to change. However, there are fundamental factors that can contribute to how users develop trust with technologies over time. This study derived a realistic trust evaluation model that incorporates security, privacy, safety, usability, reliability, and availability factors into a trust vector for a flexible measurement of trust in the user accessing the technology. Based on the derived trust model, we experiment using quantitative method whether the users are willing to trust voice biometric authentication method over PIN, fingerprint, and token-based authentication and hence would be inclined to adopt and utilize it as a means of user authentication to access technology. We applied the Kruskal-Wallis H test and the post-hoc test to understand which authentication method the user trusts, based on statistical significance and which groups were found to have that statistical difference. The result of the study suggests that users have less trust with voice compared to other authentication methods especially traditional means of knowledge-based authentication such as PIN’s which consistently ranked much higher than voice in pairwise comparisons

    Appraisal of Cashless Policy on the Nigerian Financial System

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    The Central Bank of Nigeria (CBN) has been active in the inauguration of policies and schemes to foster the implementation of the cashless policy in Nigeria. However the current transition to cashless economy raises a lot of concerns with no substantial evidence yet to justify its implementation. This study was carried out in order to appraise the implementation of the cashless policy since its introduction into the Nigerian financial system in 2012 and also to examine the persistent challenges facing its implementation. In view of the above stated objective, primary data were collected with the aid of the questionnaire, which was randomly administered to 120 respondents ranging from First Bank, Zenith Bank and United Bank for Africa. The banks were selected based on their total assets and the information collected covered the activities of the CBN and that of these banks towards implementation of the cashless policy from 2012 till date.The data collected were presented and analyzed with the aid of the Statistical Package for Social Sciences (SPSS) using descriptive statistics and one-sample t-test. The results led to the conclusion that despite the need to operate cashless transactions dominating the modern Nigerian economy, the cashless policy will have the desired impact only if a lot is done to ensure the implementation of an effective cashless system

    Artificial Intelligence and Cognitive Computing

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    Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

    Selected Computing Research Papers Volume 5 June 2016

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    An Analysis of Current Computer Assisted Learning Techniques Aimed at Boosting Pass Rate Level and Interactivity of Students (Gilbert Bosilong) ........................................ 1 Evaluating the Ability of Anti-Malware to Overcome Code Obfuscation (Matthew Carson) .................................................................................................................................. 9 Evaluation of Current Research in Machine Learning Techniques Used in Anomaly-Based Network Intrusion Detection (Masego Chibaya) ..................................................... 15 A Critical Evaluation of Current Research on Techniques Aimed at Improving Search Efficiency over Encrypted Cloud Data (Kgosi Dickson) ........................................ 21 A Critical Analysis and Evaluation of Current Research on Credit Card Fraud Detection Methods (Lebogang Otto Gaboitaolelwe) .......................................................... 29 Evaluation of Research in Automatic Detection of Emotion from Facial Expressions (Olorato D. Gaonewe) ......................................................................................................... 35 A Critical Evaluation on Methods of Increasing the Detection Rate of Anti-Malware Software (Thomas Gordon) ................................................................................................ 43 An Evaluation of the Effectiveness of the Advanced Intrusion Detection Systems Utilizing Optimization on System Security Technologies (Carlos Lee) ............................ 49 An Evaluation of Current Research on Data Mining Techniques in Decision Support (Keamogetse Mojapelo) ...................................................................................................... 57 A Critical Investigation of the Cognitive Appeal and Impact of Video Games on Players (Kealeboga Charlie Mokgalo) ................................................................................ 65 Evaluation of Computing Research Aimed at Improving Virtualization Implementation in the Cloud (Keletso King Mooketsane) ................................................. 73 A Critical Evaluation of the Technology Used In Robotic Assisted Surgeries (Botshelo Keletso Mosekiemang) ....................................................................................... 79 An Evaluation of Current Bio-Metric Fingerprint Liveness Detection (George Phillipson) ........................................................................................................................... 85 A Critical Evaluation of Current Research into Malware Detection Using Neural-Network Classification (Tebogo Duduetsang Ramatebele) ................................................ 91 Evaluating Indirect Detection of Obfuscated Malware (Benjamin Stuart Roberts) ......... 101 Evaluation of Current Security Techniques for Online Banking Transactions (Annah Vickerman) ....................................................................................................................... 10

    Biometric Systems

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    Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
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