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Hiding Biometric Hiding Biometric DataDataIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 11, NOVEMBER 2003

Anil K. Jain, Fellow, IEEE, and Anil K. Jain, Fellow, IEEE, and Umut Uludag, Student Member,Umut Uludag, Student Member, IEEE IEEE

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OutlineOutline IntroductionIntroduction Application scenariosApplication scenarios Skim through data hiding methodSkim through data hiding method Experimental resultsExperimental results

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Introduction - Introduction - What’s shortcoming of What’s shortcoming of biometricbiometric The problem of ensuring the security aThe problem of ensuring the security and integrity of biometric data is criticand integrity of biometric data is criticall

Example: ID v.s. fingerprintExample: ID v.s. fingerprint

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Introduction - Introduction - 8 basic sources of attacks8 basic sources of attacks

Fake biometric

Resubmission of digital stored biometric

Feature detector could be forced to produce feature values chosen by attacker

Synthetic feature setthe matcher

could be attacked to produced high or low scores

Attack databaseChannel

attack

Alter matching result

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Skip…Skip… Encryption v.s. steganographyEncryption v.s. steganography There have been only a few published There have been only a few published papers on watermarking of fingerprint papers on watermarking of fingerprint images.images.

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Application Application scenarios(1/2)scenarios(1/2) The biometric data (The biometric data (fingerprintfingerprint minutiaeminutiae) )

that need to bethat need to be transmitted istransmitted is hidden in a hidden in a host image, whose onlyhost image, whose only function is to function is to carry the data.carry the data. 7th attack7th attack Host: Host: synthetic fingerprintsynthetic fingerprint, face, …, face, …

R. Cappelli, A. Erol, D. Maio, and D. Maltoni, “SynthR. Cappelli, A. Erol, D. Maio, and D. Maltoni, “Synthetic Fingerprint Image Generation,” Proc. 15th Int’l etic Fingerprint Image Generation,” Proc. 15th Int’l Conf. Pattern Recognition, vol. 3, pp. 475-478,Sept. 20Conf. Pattern Recognition, vol. 3, pp. 475-478,Sept. 2000.00. Encrypt++Encrypt++

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Application scenarios(2/2)Application scenarios(2/2) Hiding facial information (e.g. Hiding facial information (e.g. eigen-face coefficientseigen-face coefficients) into fingerprin) into fingerprin

t imagest images Examine fingerprint & faceExamine fingerprint & face

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Skim through data hiding mSkim through data hiding methodethod M. Kutter, F. Jordan, and F. Bossen, “Digital SiM. Kutter, F. Jordan, and F. Bossen, “Digital Signature of Color Images Using Amplitude Modugnature of Color Images Using Amplitude Modulation,” Proc. SPIE, vol. 3022, pp. 518-526, 1997.lation,” Proc. SPIE, vol. 3022, pp. 518-526, 1997. B. Gunsel, U. Uludag, and A.M. Tekalp, “RobusB. Gunsel, U. Uludag, and A.M. Tekalp, “Robust Watermarking of Fingerprint Images,” Pattert Watermarking of Fingerprint Images,” Pattern Recognition, vol. 35, no. 12, pp. 2739-2747, Den Recognition, vol. 35, no. 12, pp. 2739-2747, Dec. 2002.c. 2002.

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Skim through data Skim through data hiding methodhiding method WatermarkWatermark

1th scenario: fingerprint minutiae 9-bit1th scenario: fingerprint minutiae 9-bit X[0,N-1], Y[0,M-1], orientattion[0,359]X[0,N-1], Y[0,M-1], orientattion[0,359]

2th scenario: eigenface coefficients 4-byte2th scenario: eigenface coefficients 4-byte Random seedRandom seed Embed watermark : repeat or notEmbed watermark : repeat or not Embed reference bits 0 & 1 ?Embed reference bits 0 & 1 ?

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Skim through data hiding mSkim through data hiding methodethod Embedding functionEmbedding function

S : the value of watermark bitS : the value of watermark bit q : embedding strength (q : embedding strength ( 自訂自訂 )) PPAVAV, P, PSDSD: average and standard deviation of neighbor: average and standard deviation of neighborhood (ex. 5x5 square)hood (ex. 5x5 square) PPGMGM: gradient magnitude ?: gradient magnitude ? A, B : weightA, B : weight β: maskβ: mask

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Skim through data hiding mSkim through data hiding methodethod Decoding functionDecoding function

5x5 cross-shaped neighborhood5x5 cross-shaped neighborhood

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Experimental resultsExperimental results Highlight Highlight decoding accuracydecoding accuracy and and

matching performancematching performance

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Experimental results -Experimental results - 1th scenario 1th scenario 1th scenario : 1th scenario :

Host : 5 synthetic fingerprint, 5 face, 5 othHost : 5 synthetic fingerprint, 5 face, 5 othersers 5 minutiae data sets, 5 seed keys5 minutiae data sets, 5 seed keys q= 0.1, A = 100, B = 1000 q= 0.1, A = 100, B = 1000 17% stego image pixels are changed17% stego image pixels are changed 100% accuracy100% accuracy

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Experimental results –Experimental results – 2nd scenario 2nd scenario 2nd scenario :2nd scenario :

Fingerprint image : 300x300Fingerprint image : 300x300 Face : 150 x 130Face : 150 x 130

14 eigenface coefficients = 56 bytes14 eigenface coefficients = 56 bytes Face database : 4 x 10 face subjectsFace database : 4 x 10 face subjects MaskMask

Minutiae-based: 23x23 blockMinutiae-based: 23x23 block Ridge-based: 3x3 blockRidge-based: 3x3 block

q= 0.1, A = 100, B = 1000q= 0.1, A = 100, B = 1000 640 fingerprint images from 160 users640 fingerprint images from 160 users

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Experimental results –Experimental results – 2nd scenario 2nd scenarioOrigin fingerprint

Origin face Reconstruct eigenface

Mask minutiae

Reconstruct fingerprint from

watermarked minutiae-based

image

Mask ridge

Reconstruct fingerprint from

watermarked ridge-based

image

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Experimental results –Experimental results – 2nd scenario 2nd scenario

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The end…The end…