Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems†
Ensicaen, Normandie University, Unicaen, CNRS, GREYC, 14000 Caen, France
Author to whom correspondence should be addressed.
The paper is an extended version of our paper publisheed in Vibert, B.; Le Bars, J.M.; Charrier, C.; Rosenberger, C. In what way is it possible to impersonate you bypassing fingerprint sensors? In Proceedings of the 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 21–23 September 2016; pp. 1–4. Vibert, B.; Le Bars, J.M.; Charrier, C.; Rosenberger, C. Fingerprint Class Recognition For Securing EMV Transaction. In Proceedings of the International Conference on Information Systems Secu-rity and Privacy, Porto, Portugal, 19–21 February 2017.
Sensors 2020, 20(18), 5410; https://doi.org/10.3390/s20185410
Received: 11 August 2020 / Revised: 9 September 2020 / Accepted: 11 September 2020 / Published: 21 September 2020
(This article belongs to the Special Issue Biometric Sensing)
Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.