Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

The analysis of handwritten evidence has been used widely in courts in the United States since the 1930s (Osborn, 1946). Traditional evaluations are conducted by trained forensic examiners. More recently, there has been a movement toward objective and probability-based evaluation of evidence, and a variety of governing bodies have made explicit calls for research to support the scientific underpinnings of the field (National Research Council, 2009; President\u27s Council of Advisors on Science and Technology (US), 2016; National Institutes of Standards and Technology). This body of work makes contributions to help satisfy those needs for the evaluation of handwritten documents. We develop a framework to evaluate a questioned writing sample against a finite set of genuine writing samples from known sources. Our approach is fully automated, reducing the opportunity for cognitive biases to enter the analysis pipeline through regular examiner intervention. Our methods are able to handle all writing styles together, and result in estimated probabilities of writership based on parametric modeling. We contribute open-source datasets, code, and algorithms. A document is prepared for the evaluation processed by first being scanned and stored as an image file. The image is processed and the text within is decomposed into a sequence of disjoint graphical structures. The graphs serve as the smallest unit of writing we will consider, and features extracted from them are used as data for modeling. Chapter 2 describes the image processing steps and introduces a distance measure for the graphs. The distance measure is used in a K-means clustering algorithm (Forgy, 1965; Lloyd, 1982; Gan and Ng, 2017), which results in a clustering template with 40 exemplar structures. The primary feature we extract from each graph is a cluster assignment. We do so by comparing each graph to the template and making assignments based on the exemplar to which each graph is most similar in structure. The cluster assignment feature is used for a writer identification exercise using a Bayesian hierarchical model on a small set of 27 writers. In Chapter 3 we incorporate new data sources and a larger number of writers in the clustering algorithm to produce an updated template. A mixture component is added to the hierarchical model and we explore the relationship between a writer\u27s estimated mixing parameter and their writing style. In Chapter 4 we expand the hierarchical model to include other graph-based features, in addition to cluster assignments. We incorporate an angular feature with support on the polar coordinate system into the hierarchical modeling framework using a circular probability density function. The new model is applied and tested in three applications

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Forensic Methods and Tools for Web Environments

abstract: The Web is one of the most exciting and dynamic areas of development in today’s technology. However, with such activity, innovation, and ubiquity have come a set of new challenges for digital forensic examiners, making their jobs even more difficult. For examiners to become as effective with evidence from the Web as they currently are with more traditional evidence, they need (1) methods that guide them to know how to approach this new type of evidence and (2) tools that accommodate web environments’ unique characteristics. In this dissertation, I present my research to alleviate the difficulties forensic examiners currently face with respect to evidence originating from web environments. First, I introduce a framework for web environment forensics, which elaborates on and addresses the key challenges examiners face and outlines a method for how to approach web-based evidence. Next, I describe my work to identify extensions installed on encrypted web thin clients using only a sound understanding of these systems’ inner workings and the metadata of the encrypted files. Finally, I discuss my approach to reconstructing the timeline of events on encrypted web thin clients by using service provider APIs as a proxy for directly analyzing the device. In each of these research areas, I also introduce structured formats that I customized to accommodate the unique features of the evidence sources while also facilitating tool interoperability and information sharing.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

A Framework for Biometric and Interaction Performance Assessment of Automated Border Control Processes

Automated Border Control (ABC) in airports and land crossings utilize automated technology to verify passenger identity claims. Accuracy, interaction stability, user error, and the need for a harmonized approach to implementation are required. Two models proposed in this paper establish a global path through ABC processes. The first, the generic model, maps separately the enrolment and verification phases of an ABC scenario. This allows a standardization of the process and an exploration of variances and similarities between configurations across implementations. The second, the identity claim process, decomposes the verification phase of the generic model to an enhanced resolution of ABC implementations. Harnessing a human-biometric sensor interaction framework allows the identification and quantification of errors within the system's use, attributing these errors to either system performance or human interaction. Data from a live operational scenario are used to analyze behaviors, which aid in establishing what effect these have on system performance. Utilizing the proposed method will aid already established methods in improving the performance assessment of a system. Through analyzing interactions and possible behavioral scenarios from the live trial, it was observed that 30.96% of interactions included some major user error. Future development using our proposed framework will see technological advances for biometric systems that are able to categorize interaction errors and feedback appropriately

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field