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

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

2. Outline • Introduction • Application scenarios • Skim through data hiding method • Experimental results by pj

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

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

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

6. Application scenarios(1/2) • The biometric data (fingerprintminutiae) that need to betransmitted ishidden in a host image, whose onlyfunction is to carry the data. • 7th attack • 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. 475-478,Sept. 2000. • Encrypt++ by pj

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

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10. 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. 518-526, 1997. • B. Gunsel, U. Uludag, and A.M. Tekalp, “Robust Watermarking of Fingerprint Images,” Pattern Recognition, vol. 35, no. 12, pp. 2739-2747, Dec. 2002. by pj

11. Skim through data hiding method • Watermark • 1th scenario: fingerprint minutiae 9-bit • X[0,N-1], Y[0,M-1], orientattion[0,359] • 2th scenario: eigenface coefficients 4-byte • Random seed • Embed watermark : repeat or not • Embed reference bits 0 & 1 ? by pj

12. Skim through data hiding method • Embedding function • S : the value of watermark bit • q : embedding strength (自訂) • PAV, PSD: average and standard deviation of neighborhood (ex. 5x5 square) • PGM: gradient magnitude ? • A, B : weight • β: mask by pj

13. Skim through data hiding method • Decoding function • 5x5 cross-shaped neighborhood by pj

14. Experimental results • Highlight decoding accuracy and matching performance by pj

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

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

17. Experimental results – 2nd scenario Origin fingerprint Origin face Reconstruct eigenface Reconstruct fingerprint from watermarked minutiae-based image Mask minutiae Mask ridge Reconstruct fingerprint from watermarked ridge-based image by pj

18. Experimental results – 2nd scenario by pj

19. The end… by pj