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

2. 29.4.2003Vabo SPI '03 Brno 2 Introduction Typical Authentication: Stamps, seals, signatures, watermarks, holograms, etc., ? = ? = New technology -> better systems against forgery New technology -> better possibilities to counterfeit

3. 29.4.2003Vabo SPI '03 Brno 3 Inspiration in Digital Documents’ Techniques 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 InfoMark technology:

4. 29.4.2003Vabo SPI '03 Brno 4 ID of a paper Scanned Paper Image size is a problem... Need image features: FINGERPRINT of image f

5. 29.4.2003Vabo SPI '03 Brno 5 Paper statistics... Histogram 0.1190.1220.3280.5410.868  3321731d Pixel correlation Mutual Intensity Occurance Dist=1, 3 and 5

6. 29.4.2003Vabo 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) 2R (i,j) R R – parameter FINGERPRINT – set E of local extremes

7. 29.4.2003Vabo SPI '03 Brno 7 Moments Image f – probability density function Statistical characteristics: Moments m ks =  i  j i k j s f(i,j) FINGERPRINT – first N moments from every square a b

8. 29.4.2003Vabo SPI '03 Brno 8 Fourier coefficients Frequency domain of an image f Discrete Fourier Transform: F(u,v)=  k  l e -2  i(uk/M+vl/N). f(k,l) FINGERPRINT – first K coefficients of Fourier transform from every square

9. 29.4.2003Vabo SPI '03 Brno 9 Fingerprint similarity measurement Given 2 images f 1 and f 2 Local extremes – E 1 and E 2 |{E 1  E 2 }| / |{E 1  E 2 }| - occurence ratio Moments and Fourier coeffs – x and y Correlation coefficient   x and  y – variances of x and y

10. 29.4.2003Vabo SPI '03 Brno 10 Authentication Scheme 1. Scanning of paper in transparency mode 2. Feature extraction from image Signature: 3. Digital signature of features and document 4. Printing signature and document using InfoMark Verification: 3. Reading paper features from InfoMark 4. Comparing features and document content

11. 29.4.2003Vabo SPI '03 Brno 11 Results - Local Extremes F SizeDifferenceLow matchHigh FailRIP 37031.14%49.47%18.33%81 vs. 1 11040.90%50.47%9.57%161 vs. 1 11044.13% 55.34%11.22%167 vs. 1 39050.30%68.41%18.11%87 vs. 1 20564.98%75.16%10.18%16Gauss 5 71560.47%77.39%16.92%8Gauss 5 Extremes may differ by 3 in coordinates

12. 29.4.2003Vabo SPI '03 Brno 12 Results - Moments a x bF SizeDifferenceLow matchHigh FailNIP 32 x 322560.4470.9890.54211 vs. 1 16 x 1610240.5740.9780.40411 vs. 1 32 x 32 16 x 16 2560.4420.9890.5471G 5 10240.5670.9810.4141G 5 32 x 32 16 x 16 2560.4540.9920.53817 vs. 1 10240.5860.9850.39917 vs. 1

13. 29.4.2003Vabo SPI '03 Brno 13 Results – Fourier descriptors F SizeDifference|Low Match||High Fail|a x bN 30720.7220.7880.06716 x 163 20480.5790.6550.07616 x 162 10240.6280.7160.08716 x 161 2560.7500.9220.17232 x 321 7680.8110.9320.12232 x 323 5120.7370.9000.16332 x 322

14. 29.4.2003Vabo SPI '03 Brno 14 The End Thank you for your attention Questions?

15. 29.4.2003Vabo 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