M. Kivanc Mihcak - UVigo-Tv

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Attacks • Exactly reproduce the fiber pattern – Physically hard – May be able to produce a mold to stamp thousands – But the each genuine label should have unique pattern • Crack the secret key used to sign the pattern – Computationally infeasible given current technology
Error-rate Analysis • Current results indicate a fairly wide separation between self correlation and cross correlation P 2 σ 2 0% Rdiff cutoff σ 1 R self 100% P(false positive) = P 2 (x > cutoff), P(false negative) = P 1 (x < cutoff) Plug in some numbers based on our experiments R diff =12%, R self =89%, σ 1 = σ 2 =5%, cutoff = 50% Assuming normal distribution false positive ≈ 1.5 * 10 -14 false negative ≈ 5.6 * 10 -23 P 1
Page 1 and 2: Overview of Recent Content Authenti
Page 3 and 4: Part I: Tamper-Resistant Biometric
Page 5: How to Verify Photo IDs? • Person
Page 8: Problem! • DESIGNED FOR DIGITAL D
Page 11 and 12: Face = Linear combination of Eigenf
Page 13 and 14: • PROS: - Paper Pros & Cons - Ine
Page 16 and 17: Summary • Face + iris + fingerpri
Page 18 and 19: Motivation • Billions of dollars
Page 20 and 21: • Physical object Requirements -
Page 22 and 23: Issuing and Verifying COAs (Analogo
Page 24 and 25: Embedding Fibers Within Labels Pres
Page 26 and 27: Scanning A Label
Page 28 and 29: Receiver Operation
Page 32 and 33: Advantages • Each label is unique
Page 34 and 35: Summary • Exciting research chall
Page 36 and 37: Problem • Counterfeiting of physi
Page 38 and 39: Overview • Resulting benefits - Q
Page 40 and 41: Fragile Watermarking • FWM (Fragi
Page 42 and 43: Counterfeit Attack Scenarios • Sc
Page 44 and 45: Scanning & Reprinting Experiments H
Page 46 and 47: Original unmarked logo s Embedding
Page 48 and 49: Print-Scan Simulations Initial deco
Page 50 and 51: • Digital camera to scan and tran

M. Kivanc Mihcak - UVigo-Tv

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