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Authentication of Paper Printed Documents Using Paper Characteristics. Mat úš Mihaľák ETH Z ürich joint work with Ivan Kočiš, Infotrust Slovakia. ?. ?. =. =. Introduction. Typical Authentication: Stamps, seals, signatures, watermarks, holograms, etc.,.
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Authentication of Paper Printed Documents Using Paper Characteristics Matúš Mihaľák ETH Zürich joint work with Ivan Kočiš, Infotrust Slovakia
? ? = = Introduction • Typical Authentication: • Stamps, seals, signatures, watermarks, holograms, etc., • New technology -> better systems against forgery • New technology -> better possibilities to counterfeit Vabo SPI '03 Brno
Generally: Document is signed using public key cryptography. Signature is somehow attached to the paper document. Drawback: Photocopy of such a document is a valid document Inspiration in Digital Documents’ Techniques InfoMark technology: Vabo SPI '03 Brno
ID of a paper Image size is a problem . . . Scanned Paper Need image features: FINGERPRINT of image f Vabo SPI '03 Brno
Mutual Intensity Occurance Histogram Pixel correlation d 1 3 7 21 33 r 0.868 0.541 0.328 0.122 0.119 Dist=1, 3 and 5 Paper statistics . . . Vabo SPI '03 Brno
Local extremes • (i,j) - local extreme iff (i,j) - global extreme on subimage (i-R …i+R, j-R …j+R) • R – parameter • FINGERPRINT – set E of local extremes (i,j) R 2R 2R Vabo SPI '03 Brno
Moments a • Image f – probability density function • Statistical characteristics: Moments • mks =SiSj ik js f(i,j) b FINGERPRINT – first N moments from every square Vabo SPI '03 Brno
Fourier coefficients • Frequency domain of an image f • Discrete Fourier Transform: F(u,v)=SkSl e-2Pi(uk/M+vl/N) . f(k,l) FINGERPRINT – first K coefficients of Fourier transform from every square Vabo SPI '03 Brno
Fingerprint similarity measurement • Given 2 images f1and f2 • Local extremes – E1 and E2 • |{E1Ç E2}| / |{E1È E2}| - occurence ratio • Moments and Fourier coeffs – x and y Correlation coefficient r: sx and sy – variances of x and y Vabo SPI '03 Brno
Authentication Scheme • Scanning of paper in transparency mode • Feature extraction from image Signature: • Digital signature of features and document • Printing signature and document using InfoMark Verification: • Reading paper features from InfoMark • Comparing features and document content Vabo SPI '03 Brno
IP R High Fail Low match Difference F Size 1 vs. 1 8 18.33% 49.47% 31.14% 370 1 vs. 1 16 9.57% 50.47% 40.90% 110 7 vs. 1 8 18.11% 68.41% 50.30% 390 7 vs. 1 16 11.22% 55.34% 44.13% 110 Gauss 5 8 16.92% 77.39% 60.47% 715 Gauss 5 16 10.18% 75.16% 64.98% 205 Results - Local Extremes • Extremes may differ by 3 in coordinates Vabo SPI '03 Brno
IP N a x b High Fail Low match Difference F Size 1 vs. 1 1 16 x 16 0.404 0.978 0.574 1024 1 vs. 1 1 32 x 32 0.542 0.989 0.447 256 7 vs. 1 1 16 x 16 0.399 0.985 0.586 1024 7 vs. 1 1 32 x 32 0.538 0.992 0.454 256 G 5 1 16 x 16 0.414 0.981 0.567 1024 G 5 1 32 x 32 0.547 0.989 0.442 256 Results - Moments Vabo SPI '03 Brno
N a x b |High Fail| |Low Match| Difference F Size 1 16 x 16 0.087 0.716 0.628 1024 2 16 x 16 0.076 0.655 0.579 2048 3 16 x 16 0.067 0.788 0.722 3072 1 32 x 32 0.172 0.922 0.750 256 2 32 x 32 0.163 0.900 0.737 512 3 32 x 32 0.122 0.932 0.811 768 Results – Fourier descriptors Vabo SPI '03 Brno
The End Thank you for your attention Questions? Vabo SPI '03 Brno
Security – Local extremes • Image of the size 256 x 256 • R = 8, #extremes = 370 => P[(i,j) is extreme]<0.00565 • Benevolence ± 2 in coordinates => P[ex]<0.07057 P[>60% match] = P[61]+P[62]+..+P[100] P[k] = (370 choose k) * P[ex]k P[> 60%] < 6.81881 x 10-147 Vabo SPI '03 Brno