Robustness of signature verification systems to imitators with increasing skills

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. F. Alonso-Fernández, J. Fiérrez, A. Gilpérez, J. Galbally, J. Ortega-García, "Robustness of Signature Verification Systems to Imitators with Increasing Skills" in International Conference on Document Analysis and Recognition (ICDAR), Barcelona (Madrid), 2009, 728 - 732In this paper, we study the impact of an incremental level of skill in the forgeries against signature verification systems. Experiments are carried out using both off-line systems, involving the discrimination of signatures written on a piece of paper, and on-line systems, in which dynamic information of the signing process (such as velocity and acceleration) is also available. We use for our experiments the BiosecurID database, which contains both on-line and off-line versions of signatures, acquired in four sessions across a 4 month time span with incremental level of skill in the forgeries for different sessions. We compare several scenarios with different size and variability of the enrolment set, showing that the problem of skilled forgeries can be alleviated as we consider more signatures for enrolment.This work has been supported by the TEC2006-13141- C03-03 project of the Spanish Ministry of Science and Technology

Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition

An Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation System

In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets, where misleading practices like fake ratings are rampant, remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system's foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system's privacy facets, rendering it particularly influential in the ever-evolving digital domain.Comment: 36 pages,13 figure

Vulnerabilities and attack protection in security systems based on biometric recognition

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 200

A Survey of the European Security Market

This document synthesizes the results of the research made on the European security market. It deals with questions of interest regarding the provision of security goods and services for protecting society from terrorism and organised crime. It explores issues such as market revenues, demand and supply, industrial capabilities, technology, research and development, innovation, business strategies, competition as well as market structure, agents' conduct and economic performance. The research has been based upon desk analysis of open source information related to the security market. Economic theory and critical analysis has been applied to understand the gathered information, derive knowledge, point out key issues and assess trends and drivers that will likely shape the sector's future. The study is the outcome of the working package number 5 included in the research project A new Agenda for European Security Economics (EUSECON). This project with code number 218195 has been financed by the European Commission within the 7th European Research Framework Programme. The task has been performed by the company ISDEFE according to the scope and work plan described in the EUSECON proposal. The author wishes to express his appreciation to all the individuals that have provided input and valuable comments to this study, including anonymous referees. Any flaws or omissions contained in this document are solely the responsibility of the author.

Face recognition by means of advanced contributions in machine learning

Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

SMT goes ABMS: Developing Strategic Management Theory using Agent-Based Modelling and Simulation.

For the emerging complexity theory of strategy (CTS), organizations are complex adaptive systems able to co-evolve with their dynamic environments through interaction and response, rather than purely analysis and planning. A promising approach within the CTS context, is to focus on a strategic logic of opportunity pursuit, one in which the distributed decision-makers behave audaciously despite unpredictable, unstable environments. Although there is only emergent support for it, intriguingly organizations can perform better when these decision-makers ‘throw caution to the wind’ even at their own possible expense. Since traditional research methods have had difficulty showing how this can work over time, this research adopts a complementary method, agent-based modelling and simulation (ABMS), to examine this phenomenon. The simulation model developed here, CTS-SIM, is based on quite simple constructs, but it introduces a rich and novel externally driven environment and represents individual decision-makers as having autonomous perceptions but constrainable decision-making freedom. Its primary contribution is the illumination of core dynamics and causal mechanisms in the opportunity-transitioning process. During model construction the apparently simple concept of opportunity-transitioning turns out to be complex, and the apparently complex integration of exogenous and endogenous environments with all three views of opportunity pursuit in the entrepreneurship literature, turns out to be relatively simple. Simulation outcomes using NetLogo contribute to CTS by confirming the positive effects on agent performance of opportunistic transitioning among opportunities in highly dynamic environments. The simulations also reveal tensions among some of the chosen variables and tipping points in emergent behaviours, point to areas where theoretical clarity is currently lacking, provoke some interesting questions and open up useful avenues for future research and data collection using other methods and models. Guidance through numerous stylized facts, flexible methods, careful documentation and description are all intended to inspire interest and facilitate critical discussion and ongoing scientific work

Secure and Usable Behavioural User Authentication for Resource-Constrained Devices

Robust user authentication on small form-factor and resource-constrained smart devices, such as smartphones, wearables and IoT remains an important problem, especially as such devices are increasingly becoming stores of sensitive personal data, such as daily digital payment traces, health/wellness records and contact e-mails. Hence, a secure, usable and practical authentication mechanism to restrict access to unauthorized users is a basic requirement for such devices. Existing user authentication methods based on passwords pose a mental demand on the user's part and are not secure. Behavioural biometric based authentication provides an attractive means, which can replace passwords and provide high security and usability. To this end, we devise and study novel schemes and modalities and investigate how behaviour based user authentication can be practically realized on resource-constrained devices. In the first part of the thesis, we implemented and evaluated the performance of touch based behavioural biometric on wearables and smartphones. Our results show that touch based behavioural authentication can yield very high accuracy and a small inference time without imposing huge resource requirements on the wearable devices. The second part of the thesis focus on designing a novel hybrid scheme named BehavioCog. The hybrid scheme combined touch gestures (behavioural biometric) with challenge-response based cognitive authentication. Touch based behavioural authentication is highly usable but is prone to observation attacks. While cognitive authentication schemes are highly resistant to observation attacks but not highly usable. The hybrid scheme improves the usability of cognitive authentication and improves the security of touch based behavioural biometric at the same time. Next, we introduce and evaluate a novel behavioural biometric modality named BreathPrint based on an acoustics obtained from individual's breathing gestures. Breathing based authentication is highly usable and secure as it only requires a person to breathe and low observability makes it secure against spoofing and replay attacks. Our investigation with BreathPrint showed that it could be used for efficient real-time authentication on multiple standalone smart devices especially using deep learning models

Data-Driven Query by Vocal Percussion

The imitation of percussive sounds via the human voice is a natural and effective tool for communicating rhythmic ideas on the fly. Query by Vocal Percussion (QVP) is a subfield in Music Information Retrieval (MIR) that explores techniques to query percussive sounds using vocal imitations as input, usually plosive consonant sounds. In this way, fully automated QVP systems can help artists prototype drum patterns in a comfortable and quick way, smoothing the creative workflow as a result. This project explores the potential usefulness of recent data-driven neural network models in two of the most important tasks in QVP. Algorithms relative to Vocal Percussion Transcription (VPT) detect and classify vocal percussion sound events in a beatbox-like performance so to trigger individual drum samples. Algorithms relative to Drum Sample Retrieval by Vocalisation (DSRV) use input vocal imitations to pick appropriate drum samples from a sound library via timbral similarity. Our experiments with several kinds of data-driven deep neural networks suggest that these achieve better results in both VPT and DSRV compared to traditional data-informed approaches based on heuristic audio features. We also find that these networks, when paired with strong regularisation techniques, can still outperform data-informed approaches when data is scarce. Finally, we gather several insights relative to people’s approach to vocal percussion and how user-based algorithms are essential to better model individual differences in vocalisation styles