Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers

New authentication applications in the protection of caller ID and banknote

In the era of computers and the Internet, where almost everything is interconnected, authentication plays a crucial role in safeguarding online and offline data. As authentication systems face continuous testing from advanced attacking techniques and tools, the need for evolving authentication technology becomes imperative. In this thesis, we study attacks on authentication systems and propose countermeasures. Considering various nominated techniques, the thesis is divided into two parts. The first part introduces caller ID verification (CIV) protocol to address caller ID spoofing in telecommunication systems. This kind of attack usually follows fraud, which not only inflicts financial losses on victims but also reduces public trust in the telephone system. We propose CIV to authenticate the caller ID based on a challenge-response process. We show that spoofing can be leveraged, in conjunction with dual tone multi-frequency (DTMF), to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We conduct extensive experiments showing that our solution can work reliably across the legacy and new telephony systems, including landline, cellular and Internet protocol (IP) network, without the cooperation of telecom providers. In the second part, we present polymer substrate fingerprinting (PSF) as a method to combat counterfeiting of banknotes in the financial area. Our technique is built on the observation that the opacity coating leaves uneven thickness in the polymer substrate, resulting in random translucent patterns when a polymer banknote is back-lit by a light source. With extensive experiments, we show that our method can reliably authenticate banknotes and is robust against rough daily handling of banknotes. Furthermore, we show that the extracted fingerprints are extremely scalable to identify every polymer note circulated globally. Our method ensures that even when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences