Banknote authentication using artificial neural network

Counterfeit banknote is an imitation currency produced without the legal sanction of the state or government. This paper is focusing on how to classify the detection technique of counterfeit banknotes. The approach that will be implemented to solve this problem is by using the method of Artificial Neutral Network. In this research, the author prefers to use back-propagation training in Artificial Neural network. The instrumentation used, is the MATLAB’s GUI application that will be designed and developed to examine and identify the authentication of banknotes. The sample data was provided by the Center of Machine Learning and Intelligent System database. In the process to get the result, the author decides to classify this sample of banknotes data into training, testing and validation

Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire

Fuzzy Logic Weighted Averaging Algorithm for Malaysian Banknotes Reader Featuring Counterfeit Detection

This paper proposed a novel fuzzy logic weighted averaging (FLWA) algorithm in image processing techniques to detect counterfeit Malaysian banknotes. Image acquisition techniques on banknote position detection and re-adjustment, image pre-processing techniques, feature extraction methods on Malaysian banknotes’ watermarks are also covered in the paper. The FLWA Algorithm has the advantage of a much simpler model since it is a human guidance learning algorithm that does not require enrolment process to get the specific weights for each security feature. Each security feature is treated with equal weight. The experimental results also shown that FLWA model also outperform the MobileNet model and VGG16 model in Malaysian banknotes’ counterfeit detection. It has a distinct advantage over earlier or current banknote counterfeit detection techniques in that it adopted the known watermarks features, with known machine learning techniques to identify real Malaysian banknotes and detect those counterfeit Malaysian banknotes

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

Probabilistic multiple kernel learning

The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels

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

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario

Measurement to Intelligence: Feature Extraction, Modeling and Predictive Analysis of Asymmetric Conflict Events

The conflict events that comprise asymmetric warfare are a primary killer of both combatants and civilians on the modern battlefield. Improvised explosive devices (IED) and direct fire (DF), the most common of these attacks, claim thousands of lives as conventional and unconventional forces clash. Computer-based predictive analysis can be used to identify locations that are useful for these events, potentially providing the awareness needed to disrupt or avoid attacks before they are launched. In this dissertation, I propose an analytical framework for predictive analysis of asymmetric conflict events. This framework incorporates a tactics-aware system model based on attacker roles that is populated with a set of geomorphometric and visibility-constrained features describing terrain and proximity to necessary supporting structures. Features that identify and assess the utility of terrain for use by risk-averse attackers are important contributors to the model. Statistical learning is used to extract spatially and temporally constrained tactical patterns. These patterns are then used to predict the utility of future or unvisited locations for conflict events. Major contributions of this dissertation include: (1) A concise, accurate feature representation of conflict events in non-urban environments; (2) A system model based on attacker roles that captures the tactical patterns of conflict events; (3) Accurate conflict event classification algorithms that support predictive analysis; and (4) A novel method for detecting and describing features that support risk-averse attackers. The framework has been implemented and tested on real-world IED and DF data collected from the conflict in Afghanistan in 2011-2012. Several learning techniques are assessed using two dimensionality reduction schemes under a variety of spatial, temporal and combined constraints. A resource-unconstrained version of the framework accurately predicts conflict events across a wide range of terrain types and over the 19 months covered by available data. A limited version of the framework that assumes less computational capability provides useful predictive analysis that can be performed in mobile and resource constrained environments

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection