Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

Biometric ID Cybersurveillance

The implementation of a universal digitalized biometric ID system risks normalizing and integrating mass cybersurveillance into the daily lives of ordinary citizens. ID documents such as driver’s licenses in some states and all U.S. passports are now implanted with radio frequency identification (RFID) technology. In recent proposals, Congress has considered implementing a digitalized biometric identification card—such as a biometric-based, “high-tech” Social Security Card—which may eventually lead to the development of a universal multimodal biometric database (e.g., the collection of the digital photos, fingerprints, iris scans, and/or DNA of all citizens and noncitizens). Such “hightech” IDs, once merged with GPS-RFID tracking technology, would facilitate exponentially a convergence of cybersurveillance-body tracking and data surveillance, or dataveillance-biographical tracking. Yet, the existing Fourth Amendment jurisprudence is tethered to a “reasonable expectation of privacy” test that does not appear to restrain the comprehensive, suspicionless amassing of databases that concern the biometric data, movements, activities, and other personally identifiable information of individuals. In this Article, I initiate a project to explore the constitutional and other legal consequences of big data cybersurveillance generally and mass biometric dataveillance in particular. This Article focuses on how biometric data is increasingly incorporated into identity management systems through bureaucratized cybersurveillance or the normalization of cybersurveillance through the daily course of business and integrated forms of governance

The New Employment Verification Act: The Functionality and Constitutionality of Biometrics in the Hiring Process Note

In 1990, Congress created the U.S. Commission on Immigration Reform to assess and make recommendations regarding the implementation and impact of U.S. immigration policy. Unanimously, the Commission proposed employment-based immigration reforms that have lead to the creation of E-Verify, an Internet-based electronic verification system used by employers to verify a prospective worker’s eligibility. Today, the system compares a prospective worker’s identification information, such as her name, date of birth, and social security number with information contained in databases housed by the Department of Homeland Security and Social Security Administration. Several members of Congress, however, have proposed legislation that would require prospective workers to submit biometric information to curb identity fraud and existing shortfalls in the verification process. This Note examines the practical and legal implications of a nationally mandated biometric verification system and whether such a system is constitutionally viable under current Fourth Amendment jurisprudence. Ultimately this Note argues that no matter how unsettling the collection of biometric information by the government may be, at least in the employment hiring context, a nationally mandated biometric verification system will most likely pass constitutional muster

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Large-scale Biometrics Deployment in Europe: Identifying Challenges and Threats

With large-scale biometrics deployment in the EU still in its infancy and with stakeholders racing to position themselves in view of the lucrative market that is forecasted, a study to identify challenges and threats that need to be dealt with was launched. This is the result: a report on Biometrics large-scale Deployment in Europe. The report tackles three main issues namely, the status, security / privacy and testing / certification processes. A survey was launched so as to help reveal the actual status of Biometrics large-scale Deployment initiatives in EU. The main outcome of the survey was that an open dissemination of implementation results policy is needed mainly on deployment plans, strategies, barriers and best practices. The security/ privacy challenges study identified a number of issues, the most important of which were related to proportionality and compliance to the existing regulatory framework while at the same time it revealed an important number of related actions aiming at ensuring both data security and privacy. The aim of the Bio Testing Europe study was double: to identify and collect comparable and certified results under different technologies, vendors and environments situations and to feed in this information to animate discussion among the members of a European network which would enhance the European testing and certification capacity. The study presents an integrated picture of the identified issues as well as a number of recommendations. With some of the systems that are being implemented involving millions of individuals as target users it is important for policy makers to adopt some of the options presented so as to address the identified through the study challengesJRC.J.4-Information Societ

Postmortem iris recognition and its application in human identification

Iris recognition is a validated and non-invasive human identification technology currently implemented for the purposes of surveillance and security (i.e. border control, schools, military). Similar to deoxyribonucleic acid (DNA), irises are a highly individualizing component of the human body. Based on a lack of genetic penetrance, irises are unique between an individual’s left and right iris and between identical twins, proving to be more individualizing than DNA. At this time, little to no research has been conducted on the use of postmortem iris scanning as a biometric measurement of identification. The purpose of this pilot study is to explore the use of iris recognition as a tool for postmortem identification. Objectives of the study include determining whether current iris recognition technology can locate and detect iris codes in postmortem globes, and if iris scans collected at different postmortem time intervals can be identified as the same iris initially enrolled. Data from 43 decedents involving 148 subsequent iris scans demonstrated a subsequent match rate of approximately 80%, supporting the theory that iris recognition technology is capable of detecting and identifying an individual’s iris code in a postmortem setting. A chi-square test of independence showed no significant difference between match outcomes and the globe scanned (left vs. right), and gender had no bearing on the match outcome. There was a significant relationship between iris color and match outcome, with blue/gray eyes yielding a lower match rate (59%) compared to brown (82%) or green/hazel eyes (88%), however, the sample size of blue/gray eyes in this study was not large enough to draw a meaningful conclusion. An isolated case involving an antemortem initial scan collected from an individual on life support yielded an accurate identification (match) with a subsequent scan captured at approximately 10 hours postmortem. Falsely rejected subsequent iris scans or "no match" results occurred in about 20% of scans; they were observed at each PMI range and varied from 19-30%. The false reject rate is too high to reliably establish non-identity when used alone and ideally would be significantly lower prior to implementation in a forensic setting; however, a "no match" could be confirmed using another method. Importantly, the data showed a false match rate or false accept rate (FAR) of zero, a result consistent with previous iris recognition studies in living individuals. The preliminary results of this pilot study demonstrate a plausible role for iris recognition in postmortem human identification. Implementation of a universal iris recognition database would benefit the medicolegal death investigation and forensic pathology communities, and has potential applications to other situations such as missing persons and human trafficking cases

Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems

[EN] Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level