R AP G

Pattern Recognition and Applications Group Department of Electrical and Electronic Engineering University of Cagliari, Italy

Robustness of Multimodal Biometric Systems under Realistic Spoof Attacks

against All Traits

Zahid Akhtar, Battista Biggio, Giorgio Fumera, Gian Luca Marcialis

•  Multimodal biometric system

•  Evaluation of robustness of multimodal systems under spoof attacks

•  Some experimental results

Outline

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Biometric systems

•  Unimodal Biometrics System

•  Multimodal Biometrics System

Sensor Matcher Feature Extractor

Database

Decision score ≥ Threshold Genuine

score < Threshold Impostor

Fingerprint Matcher

Face Matcher

Decision

Sensor and Feature Ext.

Sensor and Feature Ext.

score ≥ Threshold Genuine

score < Threshold Impostor

Score Fusion Rule f(scorefingerprint , scoreface)

scoreface

scorefingerprint

Database

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•  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?

Spoof attacks

Aim of our work

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•  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.

Aim of our work

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•  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

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

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Experimental setting •  Spoofed (Fake) traits production

•  Fake fingerprints by “consensual method” •  mould: plasticine-like material •  cast: two-compound mixture of liquid silicon

•  Fake faces by “photo attack” •  photo displayed on a laptop screen to camera

Live Spoofed (Fake)

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Live Spoofed (Fake)

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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)

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Experimental Results •  Detection Error Trade-off (DET) curves:

•  False Rejection rate (FRR) vs. false acceptance rate (FAR)

•  Performance of multimodal systems improved under no spoofing attacks with the exception of Sum rule

10!1

100

101

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10!1

100

101

102

FAR (%)

FR

R (

%)

Sum

fing.+facefing.face

10!1

100

101

102

10!1

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101

102

FAR (%)

FR

R (

%)

LLR

fing.+facefing.face

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Experimental Results

•  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

10!1

100

101

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10!1

100

101

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FAR (%)

FR

R (

%)

Sum

fing.+facefing.+face spooffing.fing. spooffaceface spoof

10!1

100

101

102

10!1

100

101

102

FAR (%)

FR

R (

%)

LLR

fing.+facefing.+face spooffing.fing. spooffaceface spoof

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Experimental Results

•  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

10!1

100

101

102

10!1

100

101

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FAR (%)

FR

R (

%)

Sum

fing.+facefing.+face spoofFAR=FRR

10−1

100

101

102

10−1

100

101

102

FAR (%)

FR

R (

%)

LLR

fing.+facefing.+face spoofFAR=FRR

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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

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Thank you