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Authentication of Paper Printed Documents Using Paper Characteristics

Explore a novel authentication method for paper documents by leveraging unique paper features and advanced technology against forgery. Discover how image analysis, local extremes, moments, Fourier descriptors, and fingerprint similarity measurements can enhance document security. See the results from Vabo SPI '03 Brno conference on authentication schemes and feature verification.

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Authentication of Paper Printed Documents Using Paper Characteristics

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  1. Authentication of Paper Printed Documents Using Paper Characteristics Matúš Mihaľák ETH Zürich joint work with Ivan Kočiš, Infotrust Slovakia

  2. ? ? = = 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

  3. 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

  4. ID of a paper Image size is a problem . . . Scanned Paper Need image features: FINGERPRINT of image f Vabo SPI '03 Brno

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. The End Thank you for your attention Questions? Vabo SPI '03 Brno

  15. 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

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