Automatic Detection of Handwriting forgery

1. Automatic Detection of Handwriting forgery Dr. Sung-Hyuk Cha & Dr. Charlies C. Tappert School of Computer Science & Information Systems

2. Recognition Examination Personality identification (Graphology) On-line Off-line Writer Identification Writer Verification Natural Writing Forgery Disguised Writing Handwriting Analysis Taxonomy Analysis of Handwriting

3. Overview • Background • Differences b/w authentic handwriting & forgery • Measure of Wrinkliness • Automatic Forgery Detection Model • Conclusion

4. Legal Motivation To determine the Validity of Individuality in Handwriting Daubert vs. Merrell Dow (1993) testing, peer review, error rates Frye vs. US (1923) scientific community U.S. vs. Starzecpyzel (1995) “skilled” testimony GE vs. Joiner (1997) weight of evidence Kumho vs.Carmichael (1999) reliability standard

5. Individuality of Handwriting Each person writes differently.

6. Authentic vs. Forgeries (b) Forgeries of (a) (a) Authentic handwriting samples from one writer

7. 3 Differences b/w authentic & forgery 1. Shape 2. Pressure 3. Speed

8. Angular and Magnitude Type Element String Image Stroke Direction Stroke Width Angular Magnitude

9. Stroke Width Extraction w7 w8 w9 w10 w1 w2 w3 w4 w5 w6 w7 w6 w5 w4 w3 w2 w1 2.83 2.83 2.83 5 3 6 5 4 5 5 4.24 4.24 4.24 2.83 4.24 4.24 4.24 min(wi) = 3 min(wi) = 2.83 (a) Vertical & horizontal stroke width (b) Diagonal stroke width

10. Fractal: How Long is a Coastline?

11. Fractal: How wrinkly is the Coastline of Britain?

12. Fractal: How wrinkly is Handwriting? (b) (a) (a) Number of in the boundary = 69 (b) Number of in the boundary = 32

13. Fractal: Measure of Wrinkliness

14. Computational Features (d-e) ascender & descender

15. Computational Features (f) stroke width (g-i) projected histogram and gradient histogram

16. Automatic Forgery Detection Model sample1 by x sample2 by x sample1 by x Forgery of x by y Feature Extractor Distance computing d-dimensional within-authentic- handwriting distance set d-dimensional between-authentic- handwriting & forgery distance set

17. Feature distances Truth Inputs & Truth cent slant wid zone side-h bot-h grad A A A A A A F F F F F F .49 .70 .71 .13 .47 .32 .21 .49 .75 .70 .26 .54 .35 .18 .49 .67 .74 .23 .48 .32 .22 .72 .33 .47 .66 .60 .42 .10 .74 .33 .48 .60 .59 .45 .10 .79 .36 .54 .60 .59 .52 .09 .30 .61 .66 .70 .71 .57 .10 .42 .72 .64 .67 .74 .53 .10 .40 .75 .67 .75 .70 .54 .11 .30 .60 .59 .66 .60 .36 .10 .32 .60 .59 .60 .59 .39 .10 .30 .66 .60 .60 .59 .34 .09

18. Artificial Neural Network Authentic sample from a known source Feature extraction Distance compu- tation Original/ Forgery? Handwriting sample in question

19. d ( , ) Decision boundary d ( , ) forgery identified authentic authentic identified as forgery Distributions and Errors within authentic distance between authentic & forgery distance

20. Design of Experiment between class within class Random selection 180 60 dichotomizer dichotomizer d-error d’-error s-error s’-error estimate

21. Conclusion • Authentic handwriting and forgery handwritten word images were collected. • Differences b/w authentic handwriting and forgery • Measure of Wrinkliness • Automatic Forgery Detection Model using the dichotomy approach. • Further quantitative study with more samples is necessary.

22. The End Thank you. http://www.csis.pace.edu/~scha/handwriting.html