Biometrics -- Using Fingerprints for Authentication

1. Biometrics -- Using Fingerprints for Authentication Todd Andel & Cyndi Roberts CIS 5370 – Computer Security Spring 2005 10 March 2014

2. Overview • Authentication Overview • Passwords, biometrics • Fingerprints for authentication • Features & matching • Live-scanning of fingerprints • Attacks • Disadvantages of fingerprint authentication • Fake finger, Trojan horse, replay, coercion

3. Authentication Overview • Authentication: • Process of verifying identity • Supports both the confidentiality & integrity of the CIA model Confidentiality Integrity Ref: class notes

4. Authentication Overview • Passwords • Most common • In theory strong: (e.g. 268 aprrox 2*1011) • In practice weak: (e.g. dictionary words, related words)

5. Authentication Overview • Biometrics • Physiological • Iris • Fingerprint (including nail) • Hand (including knuckle, palm, vascular) • Face • Voice • Retina • DNA • Even Odor, Earlobe, Sweat pore, Lips • Behavioral (patterns) • Signature • Keystroke • Voice • Gait Ref: DoD Biometrics Management Office

6. Fingerprints for Authentication • Two premises for fingerprint identification • Fingerprint details are permanent • Fingerprints are unique • Recent challenges to this claim Ref: On the Individuality of Fingerprints

7. Features & Matching • Matching Techniques • Correlation based • Ridge feature based • Minutiae based • Features of a fingerprint Ref: On the Individuality of Fingerprints

8. Features & Matching • Minutiae matching • Probability that two different fingerprints will share 12 of 36 minutiae points: 6.1 x 10-8 • Quality of automated matching • Based on number of matches:  • verification vs. identification • False positive: imposter matches >  • False negative: valid user matches < 

9. Features & Matching • a: valid match • 39 points left • 42 points right • 36 matches • b: false positive • 64 points left • 65 points right • 25 matches Ref: On the Individuality of Fingerprints

10. Live-scanning of Fingerprints • Live-scan fingerprint sensing • Three sensor types: optical, solid-state, ultrasound Ref: Handbook of Fingerprint Recognition

11. Live-scanning of Fingerprints • Optical Sensors: • “Picture” • Frustrated total internal reflection (FTIR), optical fibers, electro-optical, direct reading Ref: Fingerprint Classification and Matching Handbook of Fingerprint Recognition

12. Live-scanning of Fingerprints • Solid-State Sensors: • Direct conversion to electronic signal • Capacitive, thermal, electric field, piezoelectric Ref: Fingerprint Classification and Matching Handbook of Fingerprint Recognition

13. Live-scanning of Fingerprints • Ultrasound Sensors: • Based on acoustic signaling • Not yet mature Ref: Handbook of Fingerprint Recognition

14. Attacks on Fingerprint Authentication Systems • Attacks focus on the disadvantages of fingerprint- based recognition: • While distinctive, fingerprints are not secret • Latent fingerprints are left on everything a person touches • With only 10 fingerprints, if one is compromised by theft of a template, it can be replaced a very limited number of times (unlike a password that can be reset as often as desired) Ref: Handbook of Fingerprint Recognition

15. Fingerprint Authentication System Model This model of a fingerprint authentication system shows the 8 points of attack generally recognized by security experts Ref: Handbook of Fingerprint Recognition

16. Attack at Fingerprint Scanner • 1.Destruction of Scanner Surface • 2.Fake Finger attack Image ‘a’ – Rubber Stamp made from a finger print image Image ‘b’ – Wafer thin plastic sheet containing a three-dimensional replication of a fingerprint Ref: Handbook of Fingerprint Recognition

17. Destruction of Scanner Surface • Ruggedness is important • Weather • Keyless car entry system as opposed to e-Commerce application • Glass/Plastic surfaces covered can be easily scratched or broken • Chip-based sensors can be damaged by electrostatic discharge

18. Fake Finger Attacks • Most common method is to build an accurate three-dimensional model using the latent print from a legitimate user. • Latent fingerprints are formed when a thin film of sweat and grease are left on a surface. Can be colored with dye and lifted • Legitimate user can be in collusion or coerced • Models made using latex rubber membrane, glue impression, gelatin • Research done in 2000 – latent print used to produce silicone cement fake finger was accepted by 5 out of 6 commercial scanners on the first try. The sixth scanner accepted the print on the second try. Ref: Attacks on biometric systems: a case study in fingerprints

19. Trojan Horse Attacks • Attack can be launched at scanner, feature extractor, matcher, or system database • Program disguises itself as something else • Device will not recognize that it is sending or receiving information from a source that is not trusted • Generates false results Ref: Handbook of Fingerprint Recognition

20. Replay Attacks • Information intercepted from communication channels between modules is re-issued at a later time in an attempt to fool the system • Information moving across channels must be secured via: • Encryption and digital signatures • Timestamp and challenge response • Digitally signing fingerprint images/features

21. Attacks on Cancelable/Private Biometrics • One of the most problematic vulnerabilities of biometrics • Once a template or image is compromised, it cannot be reissued, updated, or destroyed • Can be prevented by having template or image transformed into another representation by using a non-invertible transform such as a one-way hash function paired with a verification function

22. Attacks Using Coercion • Legitimate users can be forced to identify themselves to a fingerprint-based recognition system • This cannot be detected by fake finger detection modules or cryptographic techniques • Could be prevented by having two fingerprints on file....one default, one for panic situations that would trigger security measures unnoticeable by thief

23. Summary • Biometrics is a growing field with many exciting discoveries on the horizon • However, until more secure systems can be developed, fingerprint recognition systems should be used in conjunction with another type of user identification to bolster their security Ref: On the Individuality of Fingerprints

24. References • Department of Defense, Biometrics Management Office http://www.biometrics.dod.mil • S. Pankanti, S. Prabhakar, and A. K. Jain, "On the Individuality of Fingerprints", IEEE Transactions on PAMI, Vol. 24, No. 8, pp. 1010-1025, 2002. • C. Barral, J.S. Coron, D. Naccache, “Externalized Fingerprint Matching”, Lecture Notes in Computer Science, Volume 3072, Jul 2004, Pages 309 – 315 • U. Uludag and A.K. Jain, "Attacks on biometric systems: a case study in fingerprints", Proc. SPIE-EI 2004 , pp. 622-633, San Jose, CA, January 18-22, 2004 • T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino,”Impaact of Artificial Gummy Fingers on Fingerprint Systems”, Proc. Of SPIE, Optical Security and Counterfeit Deterrence Techniques IV, vol 4677, pp.275-289, 2002 • D. Maltoni, et. Al,” Handbook of fingerprint recognition”, New York : Springer, 2003 • A. K. Jain and S. Pankanti. “Fingerprint classification and matching,” In A. Bovik, editor, Handbook for Image and Video Processing. Academic Press, April 2000. • G. Bebis, T. Deaconu, and M. Georgiopoulos, “Fingerprint identification using Delaunay triangulation,” 1999 Int. Conf. on Information Intelligence and Systems, pp. 452-459, 1999.

25. Questions