Abstract. Biometric Hash algorithms, also called BioHash, are mainly designed to ensure template protection to its biometric raw data. To assure reproducibility, BioHash algorithms provide a certain level of robustness against input variability to ensure high reproduction rates by compensating for intra-class variation of the biometric raw data. This concept can be a potential vulnerability. In this paper, we want to reflect such vulnerability of a specific Biometric Hash algorithm for handwriting, which was introduced in [1], consider and discuss possible attempts to exploit these flaws. We introduce a new reconstruction approach, which exploits this vulnerability; to generate artificial raw data out of a reference BioHash. Motivated by work from Cappelli et al. for fingerprint modality in [6] further studied in [3], where such an artificially generated raw data has the property of producing false positive recognitions, although they may not necessarily be visually similar. Our new approach for handwriting is based on genetic algorithms combined with user interaction in using a design vulnerability of the BioHash with an attack corresponding to cipher-text-only attack with side information as system parameters from BioHash. To show the general validity of our concept, in first experiments we evaluate using 60 raw data sets (5 individuals overall) consisting of two different handwritten semantics (arbitrary Symbol and fixed PIN). Experimental results demonstrate that reconstructed raw data produces an EERreconstr. in the range from 30% to 75%, as compared to nonattacked inter-class EERinter-class of 5% to 10% and handwritten PIN semantic can be better reconstructed than the Symbol semantic using this new technique. The security flaws of the Biometric Hash algorithm are pointed out and possible countermeasures are proposed.
In biometrics the variance between data acquired from the same user and same trait is not only based on different sensors or user's form of the day, but it also depends on an aging factor. Over time the biological characteristics of a human body changes. This leads to physical and mental alternations, which may have significant influence on the biometric authentication process. In order to parameterize a biometric system, the study of the degree of aging's influence is an important step. In this paper we provide an experimental evaluation on the influence of changes of handwriting biometrics by acquiring data from writers in three sessions with a time difference of one month each. The aim is to analyze the potential impact of aging processes on different written content within a biometric handwriting system in terms of authentication performance. In the worst case, the equal error rate determined on verification data acquired two month after the reference data (EER = 0.162) is four times higher than the equal error rate calculated based on reference and verification data from the first session (EER = 0.041).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.