Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Learning features for offline handwritten signature verification

Handwritten signatures are the most socially and legally accepted means for identifying a person. Over the last few decades, several researchers have approached the problem of automating their recognition, using a variety of techniques from machine learning and pattern recognition. In particular, most of the research effort has been devoted to obtaining good feature representations for signatures, by designing new feature extractors, as well as experimenting with feature extractors developed for other purposes. To this end, researchers have used insights from graphology, computer vision, signal processing, among other areas. In spite of the advancements in the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular individual) is still an open research problem. In this thesis, we propose to address this problem from another perspective, by learning the feature representations directly from signature images. The hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data. As a first contribution, we propose a method to learn Writer-Independent features using a surrogate objective, followed by training Writer-Dependent classifiers using the learned features. Furthermore, we define an extension that allows leveraging the knowledge of skilled forgeries (from a subset of users) in the feature learning process. We observed that such features generalize well to new users, obtaining state-of-the-art results on four widely used datasets in the literature. As a second contribution, we investigate three issues of signature verification systems: (i) learning a fixed-sized vector representation for signatures of varied size; (ii) analyzing the impact of the resolution of the scanned signatures in system performance and (iii) how features generalize to new operating conditions with and without fine-tuning. We propose methods to handle signatures of varied size and our experiments show results comparable to state-of-theart while removing the requirement that all input images have the same size. As a third contribution, we propose to formulate the problem of signature verification as a meta-learning problem. This formulation also learns directly from signatures images, and allows the direct optimization of the objective (separating genuine signatures and skilled forgeries), instead of relying on surrogate objectives for learning the features. Furthermore, we show that this method is naturally extended to formulate the adaptation (training) for new users as one-class classification. As a fourth contribution, we analyze the limitations of these systems in an Adversarial Machine Learning setting, where an active adversary attempts to disrupt the system. We characterize new threats posed by Adversarial Examples on a taxonomy of threats to biometric systems, and conduct extensive experiments to evaluate the success of attacks under different scenarios of attacker’s goals and knowledge of the system under attack. We observed that both systems that rely on handcrafted features, as well as those using learned features, are susceptible to adversarial attacks in a wide range of scenarios, including partial-knowledge scenarios where the attacker does not have full access to the trained classifiers. While some defenses proposed in the literature increase the robustness of the systems, this research highlights the scenarios where such systems are still vulnerable