Robustness of Multimodal Biometric      Systems under Realistic Spoof Attacks               against All Traits     Zahid A...
Outline• Multimodal biometric system• Evaluation of robustness of multimodal systems under spoof attacks• Some experimenta...
Biometric systems• Unimodal Biometrics System                                                                             ...
Spoof attacks•  Spoof attack : attacks at the user interface•  Presentation of a fake biometric trait•  Solutions:    •  L...
Aim of our work•  State-of-the-art:    • Fabrication of fake traits is a cumbersome task    • Robustness evaluation of mul...
Aim of our work• Main goal:    • Robustness evaluation methods under spoof attacks in realistic scenarios      without fab...
Experimental setting•  Data set:    • Two separate data sets of faces and fingerprints    • Chimerical multimodal data set...
Experimental setting•  Spoofed (Fake) traits production    •  Fake fingerprints by “consensual method”        • mould: pla...
Experimental setting•  Score fusion rules:    •  Sum :                  scorefused = scorefingerprint + scoreface    •  Pr...
Experimental Results               •  Detection Error Trade-off (DET) curves:                    • False Rejection rate (F...
Experimental Results                            Sum                                                            LLR        ...
Experimental Results                             Sum                                                            LLR       ...
Conclusions•  State-of-the-art: “worst-case” scenario•  Evidence of two common beliefs under spoof attacks:         • Mult...
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Robustness of Multimodal Biometric Systems under Realistic Spoof Attacks against All Traits

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Transcript of "Robustness of Multimodal Biometric Systems under Realistic Spoof Attacks against All Traits"

  1. 1. Robustness of Multimodal Biometric Systems under Realistic Spoof Attacks against All Traits Zahid Akhtar, Battista Biggio, Giorgio Fumera, Gian Luca Marcialis Pattern Recognition and Applications GroupP R A G Department of Electrical and Electronic Engineering University of Cagliari, Italy
  2. 2. Outline• Multimodal biometric system• Evaluation of robustness of multimodal systems under spoof attacks• Some experimental results
  3. 3. Biometric systems• Unimodal Biometrics System score ≥ Threshold Genuine Sensor Feature Matcher Decision Extractor score < Threshold Impostor Database• Multimodal Biometrics System Sensor and scorefingerprint Fingerprint Feature Ext. Matcher score ≥ Threshold Genuine Score Fusion Rule Decision Database f(scorefingerprint , scoreface) score < Threshold Impostor Sensor and scoreface Face Feature Ext. Matcher 3
  4. 4. Spoof attacks•  Spoof attack : attacks at the user interface•  Presentation of a fake biometric trait•  Solutions: •  Liveness Detection Methods • Increase of false rejection rate (FRR) •  Multimodal biometric Systems  “intrinsically” robust? 4
  5. 5. Aim of our work•  State-of-the-art: • Fabrication of fake traits is a cumbersome task • Robustness evaluation of multimodal systems using simulated attacks1,2 • Substantial increase of false acceptance rate (FAR) under only one trait spoofing • Hypothesis: worst-case scenario1,2 • the attacker is able to fabricate exact replica of the genuine biometric trait • match score distribution of spoofed trait is equal to one of the genuine trait • Need of investigation of robustness against realistic (non-worst case) spoof attacks 1 R. N. Rodrigues, L. L. Ling, V. Govindaraju, “Robustness of multimodal biometric fusion methods against spoof attacks”, JVLC, 2009. 2 P. A. Johnson, B. Tan and S. Schuckers, “Multimodal Fusion Vulnerability To Non-Zero Effort (Spoof) Imposters”, WIFS, 2010. 5
  6. 6. Aim of our work• Main goal: • Robustness evaluation methods under spoof attacks in realistic scenarios without fabrication of fake biometric traits• Aim of this paper: • To investigate whether a realistic spoof attacks against all modalities can allow the attacker to crack the multimodal system • and whether the worst-case assumption is realistic 6
  7. 7. Experimental setting•  Data set: • Two separate data sets of faces and fingerprints • Chimerical multimodal data set • Live: •  No. of clients: 40 •  No. of samples per client: 40 • Spoofed (Fake): •  No. of clients: 40 •  No. of samples per client: 40
  8. 8. Experimental setting•  Spoofed (Fake) traits production •  Fake fingerprints by “consensual method” • mould: plasticine-like material • cast: two-compound mixture of liquid silicon !!!!!!!!!!!!!!!! Live Spoofed (Fake) !!!!!!! !! ! •  Fake faces by “photo attack” • photo displayed on a laptop screen to camera ! !!!!!!! !! !! Live Spoofed (Fake) ! 8 !
  9. 9. Experimental setting•  Score fusion rules: •  Sum : scorefused = scorefingerprint + scoreface •  Product : scorefused = scorefingerprint × scoreface •  Weighted sum : scorefused = w × scorefingerprint + (1-w) × scoreface •  Likelihood ratio (LLR) : p(scorefingerprint |Genuine) × p(scoreface |Genuine) p(scorefingerprint |Impostor) × p(scoreface | Impostor) 9
  10. 10. Experimental Results •  Detection Error Trade-off (DET) curves: • False Rejection rate (FRR) vs. false acceptance rate (FAR) Sum LLR 2 2 10 10 1 1 10 10 FRR (%)FRR (%) fing.+face fing.+face fing. fing. face face 0 0 10 10 −1 −1 10 −1 10 −1 0 1 2 0 1 2 10 10 10 10 10 10 10 10 FAR (%) FAR (%) •  Performance of multimodal systems improved under no spoofing attacks with the exception of Sum rule 10
  11. 11. Experimental Results Sum LLR 2 2 10 10 1 1 10 fing.+face 10 fing.+face fing.+face spoof fing.+face spoof FRR (%)FRR (%) fing. fing. fing. spoof fing. spoof 0 face 0 face 10 face spoof 10 face spoof −1 −1 10 −1 10 −1 0 1 2 0 1 2 10 10 10 10 10 10 10 10 FAR (%) FAR (%) • spoof attacks worsen considerably the performance of individual systems, allowing an attacker to crack them • spoof attacks against both traits also worsen the performance of the multimodal systems • however the considered multimodal systems are more robust than unimodal ones, under attack 11
  12. 12. Experimental Results Sum LLR 2 2 10 10 1 1 10 10 FRR (%)FRR (%) fing.+face fing.+face fing.+face spoof fing.+face spoof FAR=FRR FAR=FRR 0 0 10 10 −1 −1 10 −1 10 −1 0 1 2 0 1 2 10 10 10 10 10 10 10 10 FAR (%) FAR (%) • the performance of multimodal systems under attack is worsen considerably, which confirms that they can be cracked by spoofing all traits • the worst-case assumption is not a good approximation of realistic attacks 12
  13. 13. Conclusions•  State-of-the-art: “worst-case” scenario•  Evidence of two common beliefs under spoof attacks: • Multimodal systems can be more robust than unimodal systems • Multimodal systems can be cracked by spoofing all the fused traits even when the attacker does not fabricate worst-case scenario•  Worst-case scenario is not suitable for evaluating the performance under attack•  Ongoing works: • development of methods for evaluating robustness, without constructing data sets of spoof attacks • development of robust score fusion rules 13
  14. 14. Thank you 14
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