The document discusses novel methods for biometric template selection and updating. It presents an overview of biometric systems and the issues of template representativeness, selection, and updating. It then describes the state-of-the-art in template selection and updating, which includes supervised and semi-supervised methods. The PhD work explored this state-of-the-art and proposed new methods for template selection using editing algorithms and template updating using replacement algorithms. Experimental results are presented comparing different clustering, editing, and replacement techniques for template selection and updating in biometric systems.
Multi biometric cryptosystems based on feature-level fusionParag Tamhane
The document proposes a methodology for multi-biometric cryptosystems that performs feature-level fusion. It involves three main modules: 1) An embedding algorithm that transforms biometric features into a new representation. 2) A fusion module that combines multiple biometric features into a single fused representation. 3) A biometric cryptosystem that generates a secure sketch from the fused features and verifies users during authentication. The methodology aims to provide higher security and matching performance compared to single biometric systems. A security analysis of each module and their impact on the overall system is also discussed.
This document describes an efficient approach to identifying design patterns in source code using bit-vector algorithms. It first transforms code and patterns into string representations. It then uses an iterative bit-vector algorithm to match code strings to pattern strings and identify occurrences. The approach was tested on three programs and found to identify patterns much faster than existing constraint-based techniques, especially when incorporating approximations. Future work aims to improve precision by combining with metrics and adding more relationship and dynamic information.
Tonsillitis is a disease that can be found in every
part of the world. Moreover, it is one of the main causes
intervening for heart attack and pneumonia. It has been reported
that there are a large number of people having died because of
heart attack and pneumonia. To improve data transfer rates, this
paper proposes Gabor filter design with efficient noise reduction
and less power consumption usage is proposed in this paper.
Using textural properties of anatomical structures the filter
design is suitable for detecting the early stages of disease. The
code for Gabor filter will be developed in MATLAB
IRJET- Face Spoof Detection using Machine Learning with Colour FeaturesIRJET Journal
This document proposes a machine learning approach to detect face spoofing using color features. It extracts local texture features from face images converted to different color spaces like RGB, HSV, and YCbCr. These features along with distortion features are used to train an SVM classifier to detect genuine faces and spoofed faces like photos and videos. Prior work on face spoofing detection mainly focused on intensity and avoided chroma components, but the chroma components in color spaces are effective for distinguishing real and fake faces. The proposed approach extracts color-based texture features to help identify spoofed faces.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
This document discusses a proposed framework for securing biometric templates using encryption and error correction. It begins with an introduction to biometric systems and their use of templates to identify individuals. It then discusses existing issues with transmitting biometric templates due to noise and errors. The proposed framework encrypts biometric templates using a secret key generated from braid groups before transmission. It also uses fuzzy error correction codes to detect and correct any errors introduced during transmission. The framework aims to provide both security of biometric templates and accuracy of identification by addressing noise issues.
A biometric system uses physical or behavioral traits like fingerprints, iris scans, or voice patterns to identify individuals. It consists of a scanning device, software to convert scans into digital form and compare them to stored data, and a database containing biometric information. Biometric systems are used for travel, commerce, and government applications to provide accurate identification while saving time over traditional IDs or passwords, though challenges include issues with certain traits and high costs.
Food Distribution & Management System Using Biometric Technique (Fdms)Gyan Prakash
The document proposes a Food Distribution and Management System (FDMS) using biometric techniques to improve transparency and reduce corruption in India's Public Distribution System. The FDMS would use biometric ID cards, web interfaces, and encryption to authenticate users, track food grain distribution from procurement to fair price shops, and enable users to access transaction histories and lodge complaints. It describes the enrollment and authentication processes used to verify identities and control access to data. The system is presented as a way to eliminate intermediaries, encourage direct communication between government and citizens, and maintain transparency and accountability in food grain distribution.
Multi biometric cryptosystems based on feature-level fusionParag Tamhane
The document proposes a methodology for multi-biometric cryptosystems that performs feature-level fusion. It involves three main modules: 1) An embedding algorithm that transforms biometric features into a new representation. 2) A fusion module that combines multiple biometric features into a single fused representation. 3) A biometric cryptosystem that generates a secure sketch from the fused features and verifies users during authentication. The methodology aims to provide higher security and matching performance compared to single biometric systems. A security analysis of each module and their impact on the overall system is also discussed.
This document describes an efficient approach to identifying design patterns in source code using bit-vector algorithms. It first transforms code and patterns into string representations. It then uses an iterative bit-vector algorithm to match code strings to pattern strings and identify occurrences. The approach was tested on three programs and found to identify patterns much faster than existing constraint-based techniques, especially when incorporating approximations. Future work aims to improve precision by combining with metrics and adding more relationship and dynamic information.
Tonsillitis is a disease that can be found in every
part of the world. Moreover, it is one of the main causes
intervening for heart attack and pneumonia. It has been reported
that there are a large number of people having died because of
heart attack and pneumonia. To improve data transfer rates, this
paper proposes Gabor filter design with efficient noise reduction
and less power consumption usage is proposed in this paper.
Using textural properties of anatomical structures the filter
design is suitable for detecting the early stages of disease. The
code for Gabor filter will be developed in MATLAB
IRJET- Face Spoof Detection using Machine Learning with Colour FeaturesIRJET Journal
This document proposes a machine learning approach to detect face spoofing using color features. It extracts local texture features from face images converted to different color spaces like RGB, HSV, and YCbCr. These features along with distortion features are used to train an SVM classifier to detect genuine faces and spoofed faces like photos and videos. Prior work on face spoofing detection mainly focused on intensity and avoided chroma components, but the chroma components in color spaces are effective for distinguishing real and fake faces. The proposed approach extracts color-based texture features to help identify spoofed faces.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
This document discusses a proposed framework for securing biometric templates using encryption and error correction. It begins with an introduction to biometric systems and their use of templates to identify individuals. It then discusses existing issues with transmitting biometric templates due to noise and errors. The proposed framework encrypts biometric templates using a secret key generated from braid groups before transmission. It also uses fuzzy error correction codes to detect and correct any errors introduced during transmission. The framework aims to provide both security of biometric templates and accuracy of identification by addressing noise issues.
A biometric system uses physical or behavioral traits like fingerprints, iris scans, or voice patterns to identify individuals. It consists of a scanning device, software to convert scans into digital form and compare them to stored data, and a database containing biometric information. Biometric systems are used for travel, commerce, and government applications to provide accurate identification while saving time over traditional IDs or passwords, though challenges include issues with certain traits and high costs.
Food Distribution & Management System Using Biometric Technique (Fdms)Gyan Prakash
The document proposes a Food Distribution and Management System (FDMS) using biometric techniques to improve transparency and reduce corruption in India's Public Distribution System. The FDMS would use biometric ID cards, web interfaces, and encryption to authenticate users, track food grain distribution from procurement to fair price shops, and enable users to access transaction histories and lodge complaints. It describes the enrollment and authentication processes used to verify identities and control access to data. The system is presented as a way to eliminate intermediaries, encourage direct communication between government and citizens, and maintain transparency and accountability in food grain distribution.
This document discusses adaptive biometric systems based on template update paradigms. It provides background on biometric systems and the problems of intra-class variations affecting template representativeness over time. Standard solutions like using multiple templates or modalities are noted. The goal of the PhD study is to formulate the taxonomy of current template update methods, analyze their pros and cons, and propose novel solutions. Specifically, it will experimentally analyze and compare the performance of self-update and co-update methods in controlled and uncontrolled environments. Initial results show co-update more effectively lowers equal error rates than self-update when capturing variations from unlabeled samples in uncontrolled conditions.
This document provides an overview of biometric systems security and privacy. It defines biometrics and describes the stages of biometric identification and verification. It discusses types of biometrics like fingerprints, iris scans, and behavioral biometrics. The document outlines privacy assessments for biometric systems and security vulnerabilities like sensor attacks, replay attacks, and template modification. It also describes methods for template protection including biometric cryptosystems, cancellable biometrics, and hybrid approaches. Finally, it discusses privacy benefits in multimodal biometric systems that use multiple biometrics.
Biometrics, this presentation talks about the security using biometrics. Having a security using your constant and doesn't change over time it will be a big help for your security.
This document presents nearest bi-clusters collaborative filtering (NBCF), which improves upon traditional collaborative filtering approaches. NBCF uses biclustering to group users and items simultaneously, addressing the duality between them. It introduces a new similarity measure to achieve partial matches between users' preferences. The algorithm first performs biclustering on the training data. It then calculates similarity between a test user and biclusters to find the k-nearest biclusters. Finally, it generates recommendations by weighting items based on bicluster size and similarity. An example demonstrates how NBCF provides more accurate recommendations than one-sided approaches.
Implementation of reducing features to improve code change based bug predicti...eSAT Journals
Abstract Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality of knowledge extracted and also boost the speed of computation. Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy
Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype ...Martin Chapman
The document describes Phenoflow, a microservice architecture for defining phenotypes in a structured workflow-based model to improve portability. The model defines phenotypes as sequential steps with multiple descriptions at the abstract layer and specifies entity metadata at the functional layer. Phenoflow is an online library that generates executable Common Workflow Language implementations from definitions. Evaluating diabetes and COVID-19 phenotypes showed the structured definitions improved portability over traditional logic/code by reducing clinical and programming expertise requirements. Future work includes enhancing the library search and expanding available implementation modules.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
A Research Paper on BFO and PSO Based Movie Recommendation System | J4RV4I1016Journal For Research
The objective of this work is to assess the utility of personalized recommendation system (PRS) in the field of movie recommendation using a new model based on neural network classification and hybrid optimization algorithm. We have used advantages of both the evolutionary optimization algorithms which are Particle swarm optimization (PSO) and Bacteria foraging optimization (BFO). In its implementation a NN classification model is used to obtain a movie recommendation which predict ratings of movie. Parameters or attributes on which movie ratings are dependent are supplied by user's demographic details and movie content information. The efficiency and accuracy of proposed method is verified by multiple experiments based on the Movie Lens benchmark dataset. Hybrid optimization algorithm selects best attributes from total supplied attributes of recommendation system and gives more accurate rating with less time taken. In present scenario movie database is becoming larger so we need an optimized recommendation system for better performance in terms of time and accuracy.
A NOVEL APPROACH FOR GENERATING FACE TEMPLATE USING BDAcsandit
In identity management system, commonly used biometric recognition system needs attention
towards issue of biometric template protection as far as more reliable solution is concerned. In
view of this biometric template protection algorithm should satisfy security, discriminability and
cancelability. As no single template protection method is capable of satisfying the basic
requirements, a novel technique for face template generation and protection is proposed. The
novel approach is proposed to provide security and accuracy in new user enrollment as well as
authentication process. This novel technique takes advantage of both the hybrid approach and
the binary discriminant analysis algorithm. This algorithm is designed on the basis of random
projection, binary discriminant analysis and fuzzy commitment scheme. Three publicly available
benchmark face databases are used for evaluation. The proposed novel technique enhances the
discriminability and recognition accuracy by 80% in terms of matching score of the face images
and provides high security.
A NOVEL APPROACH FOR GENERATING FACE TEMPLATE USING BDA cscpconf
In identity management system, commonly used biometric recognition system needs attention towards issue of biometric template protection as far as more reliable solution is concerned. In view of this biometric template protection algorithm should satisfy security, discriminability and
cancelability. As no single template protection method is capable of satisfying the basic requirements, a novel technique for face template generation and protection is proposed. The
novel approach is proposed to provide security and accuracy in new user enrollment as well as authentication process. This novel technique takes advantage of both the hybrid approach and the binary discriminant analysis algorithm. This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme. Three publicly available benchmark face databases are used for evaluation. The proposed novel technique enhances the discriminability and recognition accuracy by 80% in terms of matching score of the face images and provides high security
Collaborative editing of emf ecore meta models and models conflict detection,...Amanuel Alemayehu
This document summarizes a research paper presented at the MODELSWARD 2014 conference on collaborative editing of EMF/Ecore meta-models and models. It describes the DiCoMEF framework for managing collaboration, conflict detection, and reconciliation when multiple users concurrently edit models and meta-models. DiCoMEF uses a centralized controller approach where editors make modification requests to a controller, who approves or rejects changes. It detects conflicts using operation-based differencing and provides reconciliation by prioritizing propagated changes. The framework was presented as an improvement over line-based tools by addressing models' graph structure, though scaling remains a challenge.
20090219 The case for another systems biology modelling environmentJonathan Blakes
The document discusses the need for a new systems biology modelling environment. It provides context on systems biology and existing modelling approaches and software. It then makes the case that a new modelling environment could improve the user experience for biologists by making models easier to build and refine while allowing for more complex models at larger scales. Key details on existing challenges and the proposed new environment are outlined.
The document discusses several examples of interactive machine learning systems presented at conferences, including CueFlik which allows users to create rules for image ranking, CueT which helps triage alarms, Apolo which aids exploration of network data, and Visual-FSSEM which guides unsupervised feature selection. These systems incorporate user input through active learning, labeling examples, modifying parameters, and selecting feature subsets to iteratively update machine learning models.
This document proposes a novel irreversible transformation scheme for protecting biometric templates based on chaotic maps. It discusses challenges with existing biometric template protection approaches and how chaotic maps could address these challenges. The proposed scheme uses chaotic logistic maps to generate key streams that are used to transform and encrypt original biometric templates during enrollment. The same key streams are used during authentication to transform and decrypt templates for matching. The scheme aims to provide irreversibility, unlinkability, and revocability of templates to enhance security and privacy compared to traditional approaches. Simulation results are said to show the scheme is computationally simple yet provides better security performance than standard cryptographic algorithms.
Slides used to present my paper "Multi-Privacy Biometric Protection Scheme Using Ensemble System" in PhD Conference of Department of Computing of University of Surrey.
Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.
Cancelable biometrics, a template transformation approach, attempts to provide robustness for authentication services based on biometrics. Several biometric template protection techniques represent the biometric information in binary form as it provides benefits in matching and storage. In this context, it becomes clear that often such transformed binary representations can be easily compromised and breached. In this paper, we propose an efficient non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric representations. The cancelable fingerprint templates designed by the proposed technique meets the requirements of revocability, diversity, non-invertibility and performance. The matching performance of the cancelable fingerprint templates generated using proposed technique, have improved when compared with the state-of-art methods.
IRJET - Support Vector Machine versus Naive Bayes Classifier:A Juxtaposition ...IRJET Journal
This document compares the Naive Bayes and Support Vector Machine machine learning algorithms for sentiment analysis. It discusses how each algorithm works, including vectorization, parameter tuning, and terminology related to evaluating model performance such as bias, variance, cross-validation, and ROC curves. An experiment is described that applies both algorithms to movie, product, and service reviews from public datasets to determine which performs better for sentiment classification based on various evaluation metrics like accuracy, precision, recall and F1 score. The results are analyzed to understand which algorithm may be better suited for different use cases and how future work could improve model performance.
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning - 2019 Int...Pluribus One
Slides of the tutorial held by Battista Biggio, University of Cagliari and Pluribus One Srl, during "2019 International Summer School on Machine Learning and Security (MLS)"
This document discusses adaptive biometric systems based on template update paradigms. It provides background on biometric systems and the problems of intra-class variations affecting template representativeness over time. Standard solutions like using multiple templates or modalities are noted. The goal of the PhD study is to formulate the taxonomy of current template update methods, analyze their pros and cons, and propose novel solutions. Specifically, it will experimentally analyze and compare the performance of self-update and co-update methods in controlled and uncontrolled environments. Initial results show co-update more effectively lowers equal error rates than self-update when capturing variations from unlabeled samples in uncontrolled conditions.
This document provides an overview of biometric systems security and privacy. It defines biometrics and describes the stages of biometric identification and verification. It discusses types of biometrics like fingerprints, iris scans, and behavioral biometrics. The document outlines privacy assessments for biometric systems and security vulnerabilities like sensor attacks, replay attacks, and template modification. It also describes methods for template protection including biometric cryptosystems, cancellable biometrics, and hybrid approaches. Finally, it discusses privacy benefits in multimodal biometric systems that use multiple biometrics.
Biometrics, this presentation talks about the security using biometrics. Having a security using your constant and doesn't change over time it will be a big help for your security.
This document presents nearest bi-clusters collaborative filtering (NBCF), which improves upon traditional collaborative filtering approaches. NBCF uses biclustering to group users and items simultaneously, addressing the duality between them. It introduces a new similarity measure to achieve partial matches between users' preferences. The algorithm first performs biclustering on the training data. It then calculates similarity between a test user and biclusters to find the k-nearest biclusters. Finally, it generates recommendations by weighting items based on bicluster size and similarity. An example demonstrates how NBCF provides more accurate recommendations than one-sided approaches.
Implementation of reducing features to improve code change based bug predicti...eSAT Journals
Abstract Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality of knowledge extracted and also boost the speed of computation. Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy
Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype ...Martin Chapman
The document describes Phenoflow, a microservice architecture for defining phenotypes in a structured workflow-based model to improve portability. The model defines phenotypes as sequential steps with multiple descriptions at the abstract layer and specifies entity metadata at the functional layer. Phenoflow is an online library that generates executable Common Workflow Language implementations from definitions. Evaluating diabetes and COVID-19 phenotypes showed the structured definitions improved portability over traditional logic/code by reducing clinical and programming expertise requirements. Future work includes enhancing the library search and expanding available implementation modules.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
A Research Paper on BFO and PSO Based Movie Recommendation System | J4RV4I1016Journal For Research
The objective of this work is to assess the utility of personalized recommendation system (PRS) in the field of movie recommendation using a new model based on neural network classification and hybrid optimization algorithm. We have used advantages of both the evolutionary optimization algorithms which are Particle swarm optimization (PSO) and Bacteria foraging optimization (BFO). In its implementation a NN classification model is used to obtain a movie recommendation which predict ratings of movie. Parameters or attributes on which movie ratings are dependent are supplied by user's demographic details and movie content information. The efficiency and accuracy of proposed method is verified by multiple experiments based on the Movie Lens benchmark dataset. Hybrid optimization algorithm selects best attributes from total supplied attributes of recommendation system and gives more accurate rating with less time taken. In present scenario movie database is becoming larger so we need an optimized recommendation system for better performance in terms of time and accuracy.
A NOVEL APPROACH FOR GENERATING FACE TEMPLATE USING BDAcsandit
In identity management system, commonly used biometric recognition system needs attention
towards issue of biometric template protection as far as more reliable solution is concerned. In
view of this biometric template protection algorithm should satisfy security, discriminability and
cancelability. As no single template protection method is capable of satisfying the basic
requirements, a novel technique for face template generation and protection is proposed. The
novel approach is proposed to provide security and accuracy in new user enrollment as well as
authentication process. This novel technique takes advantage of both the hybrid approach and
the binary discriminant analysis algorithm. This algorithm is designed on the basis of random
projection, binary discriminant analysis and fuzzy commitment scheme. Three publicly available
benchmark face databases are used for evaluation. The proposed novel technique enhances the
discriminability and recognition accuracy by 80% in terms of matching score of the face images
and provides high security.
A NOVEL APPROACH FOR GENERATING FACE TEMPLATE USING BDA cscpconf
In identity management system, commonly used biometric recognition system needs attention towards issue of biometric template protection as far as more reliable solution is concerned. In view of this biometric template protection algorithm should satisfy security, discriminability and
cancelability. As no single template protection method is capable of satisfying the basic requirements, a novel technique for face template generation and protection is proposed. The
novel approach is proposed to provide security and accuracy in new user enrollment as well as authentication process. This novel technique takes advantage of both the hybrid approach and the binary discriminant analysis algorithm. This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme. Three publicly available benchmark face databases are used for evaluation. The proposed novel technique enhances the discriminability and recognition accuracy by 80% in terms of matching score of the face images and provides high security
Collaborative editing of emf ecore meta models and models conflict detection,...Amanuel Alemayehu
This document summarizes a research paper presented at the MODELSWARD 2014 conference on collaborative editing of EMF/Ecore meta-models and models. It describes the DiCoMEF framework for managing collaboration, conflict detection, and reconciliation when multiple users concurrently edit models and meta-models. DiCoMEF uses a centralized controller approach where editors make modification requests to a controller, who approves or rejects changes. It detects conflicts using operation-based differencing and provides reconciliation by prioritizing propagated changes. The framework was presented as an improvement over line-based tools by addressing models' graph structure, though scaling remains a challenge.
20090219 The case for another systems biology modelling environmentJonathan Blakes
The document discusses the need for a new systems biology modelling environment. It provides context on systems biology and existing modelling approaches and software. It then makes the case that a new modelling environment could improve the user experience for biologists by making models easier to build and refine while allowing for more complex models at larger scales. Key details on existing challenges and the proposed new environment are outlined.
The document discusses several examples of interactive machine learning systems presented at conferences, including CueFlik which allows users to create rules for image ranking, CueT which helps triage alarms, Apolo which aids exploration of network data, and Visual-FSSEM which guides unsupervised feature selection. These systems incorporate user input through active learning, labeling examples, modifying parameters, and selecting feature subsets to iteratively update machine learning models.
This document proposes a novel irreversible transformation scheme for protecting biometric templates based on chaotic maps. It discusses challenges with existing biometric template protection approaches and how chaotic maps could address these challenges. The proposed scheme uses chaotic logistic maps to generate key streams that are used to transform and encrypt original biometric templates during enrollment. The same key streams are used during authentication to transform and decrypt templates for matching. The scheme aims to provide irreversibility, unlinkability, and revocability of templates to enhance security and privacy compared to traditional approaches. Simulation results are said to show the scheme is computationally simple yet provides better security performance than standard cryptographic algorithms.
Slides used to present my paper "Multi-Privacy Biometric Protection Scheme Using Ensemble System" in PhD Conference of Department of Computing of University of Surrey.
Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.
Cancelable biometrics, a template transformation approach, attempts to provide robustness for authentication services based on biometrics. Several biometric template protection techniques represent the biometric information in binary form as it provides benefits in matching and storage. In this context, it becomes clear that often such transformed binary representations can be easily compromised and breached. In this paper, we propose an efficient non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric representations. The cancelable fingerprint templates designed by the proposed technique meets the requirements of revocability, diversity, non-invertibility and performance. The matching performance of the cancelable fingerprint templates generated using proposed technique, have improved when compared with the state-of-art methods.
IRJET - Support Vector Machine versus Naive Bayes Classifier:A Juxtaposition ...IRJET Journal
This document compares the Naive Bayes and Support Vector Machine machine learning algorithms for sentiment analysis. It discusses how each algorithm works, including vectorization, parameter tuning, and terminology related to evaluating model performance such as bias, variance, cross-validation, and ROC curves. An experiment is described that applies both algorithms to movie, product, and service reviews from public datasets to determine which performs better for sentiment classification based on various evaluation metrics like accuracy, precision, recall and F1 score. The results are analyzed to understand which algorithm may be better suited for different use cases and how future work could improve model performance.
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning - 2019 Int...Pluribus One
Slides of the tutorial held by Battista Biggio, University of Cagliari and Pluribus One Srl, during "2019 International Summer School on Machine Learning and Security (MLS)"
WILD PATTERNS - Introduction to Adversarial Machine Learning - ITASEC 2019Pluribus One
1) Adversarial machine learning studies machine learning systems that operate in adversarial settings such as spam filtering, where the data source is non-neutral and can deliberately attempt to reduce classifier performance.
2) Deep learning models were found to be susceptible to adversarial examples, which are imperceptibly perturbed inputs that cause models to make incorrect predictions.
3) Studies have shown that adversarial examples generated in a digital environment can still fool models when inputs are acquired through a physical system like a camera, indicating these attacks pose a real-world threat.
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub...Pluribus One
This document discusses research into generating adversarial examples to attack the vision system of the iCub humanoid robot. The researchers were able to craft perturbed images that were misclassified by the robot despite being visually indistinguishable from the originals. They developed gradient-based optimization attacks to target specific misclassifications or induce any misclassification. Potential countermeasures include rejecting inputs that fall in the "blind spots" far from the training data. However, deep learning features are unstable, with small pixel changes mapping to large changes in the deep space. Future work aims to address this instability issue.
Secure Kernel Machines against Evasion AttacksPluribus One
This document summarizes research on developing more secure machine learning classifiers. It discusses how gradient-based and surrogate model approaches can be used to evade existing classifiers. The researchers then propose several techniques for building more robust classifiers, including using infinity-norm regularization, cost-sensitive learning, and modifying kernel parameters. Experiments on handwritten digit and spam filtering datasets show the proposed approaches improve security against evasion attacks compared to standard support vector machines.
Machine Learning under Attack: Vulnerability Exploitation and Security MeasuresPluribus One
This document summarizes research on machine learning security and adversarial attacks. It describes how machine learning systems are increasingly being used for consumer applications, but this opens them up to new security risks from skilled attackers. The document outlines different types of adversarial attacks against machine learning, including evasion attacks that aim to evade detection and poisoning attacks that aim to compromise a system's availability. It also discusses approaches for systematically evaluating the security of pattern classification systems against bounded adversaries.
Battista Biggio @ ICML 2015 - "Is Feature Selection Secure against Training D...Pluribus One
This document discusses the security of feature selection algorithms against training data poisoning attacks. It presents a framework to evaluate this, including models of the attacker's goal, knowledge, and capabilities. Experiments show that LASSO feature selection is vulnerable to poisoning attacks, which can significantly affect the selected features. The research aims to better understand these risks and develop more secure feature selection methods.
Battista Biggio @ MCS 2015, June 29 - July 1, Guenzburg, Germany: "1.5-class ...Pluribus One
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, although they have not been originally designed to cope with intelligent attackers that manipulate data at test time to evade detection.
While a number of adversary-aware learning algorithms have been proposed, they are computationally demanding and aim to counter specific kinds of adversarial data manipulation.
In this work, we overcome these limitations by proposing a multiple classifier system capable of improving security against evasion attacks at test time by learning a decision function that more tightly encloses the legitimate samples in feature space, without significantly compromising accuracy in the absence of attack. Since we combine a set of one-class and two-class classifiers to this end, we name our approach one-and-a-half-class (1.5C) classification. Our proposal is general and it can be used to improve the security of any classifier against evasion attacks at test time, as shown by the reported experiments on spam and malware detection.
Sparse Support Faces - Battista Biggio - Int'l Conf. Biometrics, ICB 2015, Ph...Pluribus One
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Freni - Ph.D. Defense Slides
1. Biometric System
Template Editing
Template Replacement
Template Editing & Replacement: novel methods
for Biometric Template Selection & Update
Biagio Freni
Advisor: Prof. Fabio Roli
Pattern Recognition and Application Group
Dept. Electrical Electronic Engineering - University of Cagliari
05 March 2010
Biagio Freni Template Editing & Replacement in Biometric
3. Biometric System
Template Editing
Template Replacement
Overture
20 January 2010 . . . A man has been founded dead in a Dubai’s
hotel.
. . . couple of days later . . . Local Police discovered that 11 main
suspects got into the country illegally using forged passports of
European citizen. Police found out that pictures in the documents
were different from legitimate owner’s pictures.
. . . 14 January 2010 just a week before the Dubai affair, EU
delegates approved — 594 vs 51, while 37 abstained — the launch
of Biometric Passport including owner’s fingerprint and face.
Biagio Freni Template Editing & Replacement in Biometric
4. Biometric System Overview
Template Editing Template Representativeness
Template Replacement State-of-the-Art: Template Selection & Update
What’s Biometric?
Biometric refers to the use of physiological or behavioural
characteristics, “unique” for each person, with the aim of
established people’s identity.
Core of Biometric System is represented by Templates.
Biagio Freni Template Editing & Replacement in Biometric
5. Biometric System Overview
Template Editing Template Representativeness
Template Replacement State-of-the-Art: Template Selection & Update
Template Selection & Update
The issue of template selection and update, in biometric
recognition systems, is twofold and is related to:
Selection during Enrollment regarding the effective creation
of representative template gallery of client populations,
keeping the number of templates as small as possible at the
same time.
Update during Authentication regarding the need of adapt
over time templates, in order to capture the variations, in the
biometric traits not presented in the time of enrollment.
Selection & Update are different problems that share the common
notion of “best representative” templates.
Biagio Freni Template Editing & Replacement in Biometric
6. Biometric System Overview
Template Editing Template Representativeness
Template Replacement State-of-the-Art: Template Selection & Update
State-of-the-Art: summary
State-of-the-Art can be summurized by following modalities 1 :
Supervised: requires human intervention to labeling data.
Semi-Supervised 2 : queries labelled by the system are used
for the task.
Offline: a bunch of semi-labelled data are stored during the
system authentication, later, they are used to update system’s
templates when the system itself is not operative.
Online: each coming query is evaluated by the system during
authentication phase, template adaptation is performed online.
1
A. Rattani, B. Freni, G.L. Marcialis, F. Roli, Template Update Methods in
Adaptive Biometric Systems: A Critical Review, ICB09, pp 847-856.
2
B. Freni, G.L. Marcialis, and F. Roli, Online and offline fingerprint
template update using minutiae: an experimental comparison, AMDO08, July,
9-11, 2008, Eds., Springer LNCS 5098, pp. 441-448.
Biagio Freni Template Editing & Replacement in Biometric
7. Biometric System Overview
Template Editing Template Representativeness
Template Replacement State-of-the-Art: Template Selection & Update
PhD work
This PhD work explored the whole S-o-A and new methods have
been proposed and published:
S-o-A: Template Update Methods in Adaptive Biometric
Systems: A Critical Review, al. et Freni, ICB09.
Supervised: Template Selection by Editing Algorithms: a
case of Study in Face Recognition, Freni et al., S+SSPR08.
Semi-Supervised
Offline: Online and offline fingerprint template update using
minutiae: an experimental comparison, Freni et al., AMDO08.
Online: Replacement algorithms for fingerprint template
update, Freni et al., ICIAR08.
For sake of time just two works are addressed in this talk Editing
methods for Template Selection and Replacement algorithms for
Template Update.
Biagio Freni Template Editing & Replacement in Biometric
8. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
Template selection in Biometric
Problem statement Given a set of N templates for a given
person, select K templates that “best” represent the owner’s
identity.
State-of-the-Art Derived from the clustering theory,
consisting in exploring each template gallery according with
two criteria: maximum similarity among templates (MDIST),
maximum variation among them (DEND).
Main Cons
1. The procedure is not fully automatic since it requires the
manual insertion of parameter K .
2. All the template gallery are resized to the same dimension K ,
without taking into account “intrinsic” difficulty of each client.
Biagio Freni Template Editing & Replacement in Biometric
9. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
SoA MDIST: maximum similarity among templates
apply to all client’s gallery
1. Compute distance between
N templates
2. For each template
compute the average
distance with the other
(N − 1)
3. Choose K templates with
smallest average distance
as new selected gallery
Biagio Freni Template Editing & Replacement in Biometric
10. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
SoA DEND: maximum variation among templates
apply to all client’s gallery
1. Generate a NxN dissimilarity
matrix DM
2. Apply Complete Link Clustering
to DM in order to generate a
Dendrogram D, using D to
identify K clusters
3. For each K cluster select the
center
4. The set of templates selected in 3.
represent a new selected gallery
Biagio Freni Template Editing & Replacement in Biometric
11. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
Novel Proposal: Template Editing for Biometric
Editing Algorithms
Editing algorithms belong to the K − NN classifier theory. K − NN
use a set of prototype to perfom classification. A pattern is
classified according to the majority of “K ” prototypes close to it.
Biometric could be seen as a “1 − NN” classifier where templates
are prototypes.
Editing consist in generating from a given Template Set T a subset
E able to maintain the same classification accuracy on T itself.
Characteristics of Editing Algorithms:
1. the procedure is completly automatic
2. build up variable length galleries accordingly with the
“difficult” of each client
3. a superior generalization ability is expected
Biagio Freni Template Editing & Replacement in Biometric
12. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
3
CNN: Condensed Nearest Neighbour
1. E ← x1, ..., xC , C number of clients, T template set, E
edited set and x1..xC are templates randomly selected from T
2. T ← T − E
3. classify T using E
4. Y set of misclassified templates in T
5. if Y = φ then
5.1 E ← E ∪ Y
5.2 T ← T − Y
5.3 repeat from point 4
6. Stop
3
P.E. Hart, The Condensed Nearest Neighbor Rule, IEEE Transactions on
Information Theory, 14, 515-516.
Biagio Freni Template Editing & Replacement in Biometric
13. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
4
RNN: Reduced Nearest Neighbour
1. E ← T
2. for each x ∈ E
2.1 E ←E −x
2.2 classify T using E
2.3 Y set of misclassified templates in T
2.4 if Y = φ then
2.4.1 E ← E ∪ x
3. Stop
4
G.W. Gates, The Reduced Nearest Neighbor Rule, IEEE Transactions on
Information Theory, 18 (3) 431-433.
Biagio Freni Template Editing & Replacement in Biometric
14. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
Data sets, Protocol and Perfomance
Data sets
Results are carried out over Equinox, public Faces Dataset.
50 clients have been randomly choosen from the dataset. Each one
made up of 100 samples. A total of 5000 faces images.
Protocol
All the images have been grouped in two equal size sets, T and t.
T has been used as Template Set and t as a complete separated
test set to assess performance.
Performance
System’s performance has been evaluated as identification
accuracy : number of correct identified queries over total number
of submitted queries.
Results are showed as mean and (std) over six runs.
Biagio Freni Template Editing & Replacement in Biometric
15. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
5
Results: Accuracy
Accuracy over a test set obtained by different selection methods.
Gallery #instances×class TEST
TRAIN 50 (0) 99.62 (0.14)
CNN 7 (3) 97.6 (0.45)
SNN 4 (3) 73.66 (3.31)
RNN 17 (9) 98.43 (0.53)
ENN 49 (1) 99.35 (0.27)
MDIST 6 (0) 94.15 (0.68)
MDIST 9 (0) 96.56 (0.58)
DEND 6 (0) 89.11 (1.39)
DEND 9 (0) 94.03 (0.70)
5
B. Freni, G.L. Marcialis, and F. Roli, Template Selection by Editing
Algorithms: a case of Study in Face Recognition, S+SSPR08, Springer
LNCS5342, 755-764.
Biagio Freni Template Editing & Replacement in Biometric
17. Biometric System Clustering Algorithms
Template Editing Editing Algorithms
Template Replacement Experimental Comparison
Summary
Editing algorithms have been showed as a good alternative to the
State-of-the-Art Template Selection techniques.
Results pointed out main characteristics of Editing algorithms:
1. Completly automatic procedures, no futher intervention is
needed by supervisor.
2. Capability to build up variable length galleries, according to
client intrinsic difficulty.
3. Superior identification accuracy.
As a step futher a combined use of both techniques could be
investigated.
Biagio Freni Template Editing & Replacement in Biometric
18. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Template Update in Biometric
Problem statement The problem is quite intuitive and
consists in making adaptive the biometric recognition systems
over the time.
Templates collected during enrollment tend to be non
representative by the time, due by the large intra-class
variation.
Performing several enrollment sessions is expensive.
State-of-the-Art
Semi-supervised paradigms exploit unlabelled samples
submitted to the system in order to find out “highly genuine”
to adapt system’s templates.
Biagio Freni Template Editing & Replacement in Biometric
19. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
S-o-A summary: Semi-Supervised Template Update
Semi-Supervised methods can be summarized by basic operations:
1. Insertion. A “highly genuine” is added into template gallery.
2. Condensing. A template gallery is “fused” in a
“super-template”.
Main Cons:
1. Sistematic use of Insertion made up long galleries. For real
systems Memory and Time of Matching are constrains.
2. Condensing absolves constrains but is less representative of
the original template galleries.
Replacement is a novel basic operation. Able to:
1. Absolve constrains of Memory and Time of Matching.
2. Assure high level of perfomance.
Biagio Freni Template Editing & Replacement in Biometric
20. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Novel Proposal: Replacement Algorithm
T c indicates the template gallery of client c.
M is the maximum number of template slots allowed.
|T c | is the length of client’s gallery.
Replacement algorithm consists in the following steps:
for each client c = 1..C
1. x ← i, i as novel input
2. s = ms(x, T c ), matching score
3. if s > threshold, “highly genuine”
3.1 if |T c | < M then T c = T c ∪ x
3.2 else T c = replace(T c , x)
Function replace is made up according to some criteria.
Biagio Freni Template Editing & Replacement in Biometric
21. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Replacement criteria
Random Novel template replaces one randomly chosen.
Naive Novel template replaces the one nearest to it.
FIFO Template galleries are managed as a First In First Out
queue. The new element supersede the oldest one.
LFU Template galleries are seen as a priority queue Least
Frequently Used. Less used template is substituted by novel
one.
MDIST applied to semi-supervised scenario. A new gallery is
created adding by a novel template. MDIST is applied to
pruned one element from the gallery.
DEND applied in semi-supervised scenario. A new gallery is
created adding by a novel template, then, a Dendrogram is
made up. Based on Dendrogram an element is removed from
the gallery.
Biagio Freni Template Editing & Replacement in Biometric
22. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Data sets, Protocol and Perfomance
Data sets
Results are carried out over 12 public Fingerprint datasets. Each
one made up of 100 clients, 8 samples per client, a total of 800 of
fingerprint images for dataset.
Protocol
50 clients have been selected as system’s users. Other 50 as
impostors. For each user 3 sets have been created L, U and T. L
refers to user’s template gallery, U as unlabelled coming inputs and
T as separeted test. U contains genuine and impostors.
Performance
Equal Error Rate has been calculated over seven runs. EER
represents the error of the verification system when a number of
false acceptances is equal to a number of false rejections.
Biagio Freni Template Editing & Replacement in Biometric
24. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
6
Results: EER vs gallery dimension M
6
B. Freni, G.L. Marcialis, F. Roli, Replacement algorithms for fingerprint
template update, 5th Int. Conf. On Image Analysis and Recognition ICIAR08,
June, 25-27, 2008, Povoa de Varzim (Portugal), A. Campihlo and M. Kamel
Eds., Springer LNCS 5112, pp. 884-893.
Biagio Freni Template Editing & Replacement in Biometric
25. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Summary
Results pointed out:
1. Less EER respect to update without replacement has been
showed.
2. Perfomance differences among replacement criteria are strong
with small “M”. Which means when strong requirements of
storing memory is a constrain.
3. MDIST outperfom other criteria, due to the fact that it
performs replacement only if it is necessary.
Biagio Freni Template Editing & Replacement in Biometric
26. Biometric System Semi-Supervised Template Update
Template Editing Template Update with Replacement
Template Replacement Results
Conclusions
Biometric plays a central role in the problem of security and
its importance is going to grow.
Template representativeness is the key for the success of a
Biometric system. Templates that “best” represent people’s
identity must be choosen during “enrollment”, as well during
the “authentication”, “highly genuine” must be detected in
the coming input queries.
Among the whole S-o-A explored in this investigation:
1. the employ of Editing for template selection during enrollment
2. the use of Replacement for template update during
authentication
Template representativeness is crucial for other important
issues in Biometric as Sensor-Interoparability in fingerprint.
This problem has been addressed too, but for sake of room
this talk was dedicated just to Editing and Replacement.
Biagio Freni Template Editing & Replacement in Biometric