2005/6/16 by pj 1 Hiding Biometric Data Hiding Biometric Data IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 11, NOVEMBER 2003 Anil K. Jain, Fellow, IEEE, and Umut Uludag, Student Member, IEEE

2005/6/16 2by pj Outline Introduction Introduction Application scenarios Application scenarios Skim through data hiding method Skim through data hiding method Experimental results Experimental results

2005/6/16 3by pj Introduction - What ’ s shortcoming of biometric The problem of ensuring the security and integrity of biometric data is critical The problem of ensuring the security and integrity of biometric data is critical Example: ID v.s. fingerprint Example: ID v.s. fingerprint

2005/6/16 4by pj Introduction - 8 basic sources of attacks Fake biometric Resubmission of digital stored biometricFeature detector could be forced to produce feature values chosen by attacker Synthetic feature set the matcher could be attacked to produced high or low scores Attack database Channel attack Alter matching result

2005/6/16 5by pj Skip … Encryption v.s. steganography Encryption v.s. steganography There have been only a few published papers on watermarking of fingerprint images. There have been only a few published papers on watermarking of fingerprint images.

2005/6/16 6by pj Application scenarios(1/2) The biometric data (fingerprint minutiae) that need to be transmitted is hidden in a host image, whose only function is to carry the data. The biometric data (fingerprint minutiae) that need to be transmitted is hidden in a host image, whose only function is to carry the data. 7th attack 7th attack Host: synthetic fingerprint, face, … Host: synthetic fingerprint, face, … R. Cappelli, A. Erol, D. Maio, and D. Maltoni, “ Synthetic Fingerprint Image Generation, ” Proc. 15th Int ’ l Conf. Pattern Recognition, vol. 3, pp ,Sept R. Cappelli, A. Erol, D. Maio, and D. Maltoni, “ Synthetic Fingerprint Image Generation, ” Proc. 15th Int ’ l Conf. Pattern Recognition, vol. 3, pp ,Sept Encrypt++ Encrypt++

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2005/6/16 8by pj Application scenarios(2/2) Hiding facial information (e.g. eigen-face coefficients) into fingerprint images Hiding facial information (e.g. eigen-face coefficients) into fingerprint imageseigen-face coefficientseigen-face coefficients Examine fingerprint & face Examine fingerprint & face

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2005/6/16 10by pj Skim through data hiding method M. Kutter, F. Jordan, and F. Bossen, “ Digital Signature of Color Images Using Amplitude Modulation, ” Proc. SPIE, vol. 3022, pp , M. Kutter, F. Jordan, and F. Bossen, “ Digital Signature of Color Images Using Amplitude Modulation, ” Proc. SPIE, vol. 3022, pp , B. Gunsel, U. Uludag, and A.M. Tekalp, “ Robust Watermarking of Fingerprint Images, ” Pattern Recognition, vol. 35, no. 12, pp , Dec B. Gunsel, U. Uludag, and A.M. Tekalp, “ Robust Watermarking of Fingerprint Images, ” Pattern Recognition, vol. 35, no. 12, pp , Dec

2005/6/16 11by pj Skim through data hiding method Watermark Watermark 1th scenario: fingerprint minutiae 9-bit 1th 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-byte 2th scenario: eigenface coefficients 4-byte Random seed Random seed Embed watermark : repeat or not Embed watermark : repeat or not Embed reference bits 0 & 1 ? Embed reference bits 0 & 1 ?

2005/6/16 12by pj Skim through data hiding method Embedding function Embedding function S : the value of watermark bit S : the value of watermark bit q : embedding strength ( 自訂 ) q : embedding strength ( 自訂 ) P AV, P SD : average and standard deviation of neighborhood (ex. 5x5 square) P AV, P SD : average and standard deviation of neighborhood (ex. 5x5 square) P GM : gradient magnitude ? P GM : gradient magnitude ? A, B : weight A, B : weight β: mask β: mask

2005/6/16 13by pj Skim through data hiding method Decoding function Decoding function 5x5 cross-shaped neighborhood 5x5 cross-shaped neighborhood

2005/6/16 14by pj Experimental results Highlight decoding accuracy and matching performance Highlight decoding accuracy and matching performance

2005/6/16 15by pj Experimental results - 1th scenario 1th scenario : 1th scenario : Host : 5 synthetic fingerprint, 5 face, 5 others Host : 5 synthetic fingerprint, 5 face, 5 others 5 minutiae data sets, 5 seed keys 5 minutiae data sets, 5 seed keys q= 0.1, A = 100, B = 1000 q= 0.1, A = 100, B = % stego image pixels are changed 17% stego image pixels are changed 100% accuracy 100% accuracy

2005/6/16 16by pj Experimental results – 2nd scenario 2nd scenario : 2nd scenario : Fingerprint image : 300x300 Fingerprint image : 300x300 Face : 150 x 130 Face : 150 x eigenface coefficients = 56 bytes 14 eigenface coefficients = 56 bytes Face database : 4 x 10 face subjects Face database : 4 x 10 face subjects Mask Mask Minutiae-based: 23x23 block Minutiae-based: 23x23 block Ridge-based: 3x3 block Ridge-based: 3x3 block q= 0.1, A = 100, B = 1000 q= 0.1, A = 100, B = fingerprint images from 160 users 640 fingerprint images from 160 users

2005/6/16 17by pj Experimental results – 2nd scenario Origin fingerprint Origin faceReconstruct eigenface Mask minutiae Reconstruct fingerprint from watermarked minutiae- based image Mask ridge Reconstruct fingerprint from watermarked ridge- based image

2005/6/16 18by pj Experimental results – 2nd scenario

2005/6/16 19by pj The end …