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

Modelling Smart Card Security Protocols in SystemC TLM

Smart cards are an example of advanced chip technology. They allow information transfer between the card holder and the system over secure networks, but they contain sensitive data related to both the card holder and the system, that has to be kept private and confidential. The objective of this work is to create an executable model of a smart card system, including the security protocols and transactions, and to examine the strengths and determine the weaknesses by running tests on the model. The security objectives have to be considered during the early stages of systems development and design, an executable model will give the designer the advantage of exploring the vulnerabilities early, and therefore enhancing the system security. The Unified Modeling Language (UML) 2.0 is used to model the smart card security protocol. The executable model is programmed in SystemC with the Transaction Level Modeling (TLM) extensions. The final model was used to examine the effectiveness of a number of authentication mechanisms with different probabilities of failure. In addition, a number of probable attacks on the current security protocol were modeled to examine the vulnerabilities. The executable model shows that the smart card system security protocols and transactions need further improvement to withstand different types of security attacks

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the ULPGC and M.G.-B. is supported by a FPU fellowship from the Spanish MECD. This work has been partially supported by projects: MCINN TEC2012-38630- C04-02, Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM-Telefónic