Forged Handwriting Detection

1. Forged Handwriting Detection Hung-Chun Chen M.S. in Computer Science Advisors: Drs. Cha and Tappert

2. Motivation • Important documents require signatures to verify the identity of the writer • Experts are required to differentiate between authentic and forged signatures • Important to develop an objective system to identify forged handwriting, or at least to identify those handwritings that are likely to be forged

3. Key Idea • It seems reasonable that successful forgers often forge handwriting shape and size by carefully copying or tracing the authentic handwriting • Forensic literature indicates that this is true

4. Hypotheses • Good forgeries – those that retain the shape and size of authentic writing – tend to be written more slowly (carefully) than authentic writing • Good forgeries are likely to be wrinklier (less smooth) than authentic handwriting

5. Methodology • Handwriting sample collection • Feature extraction • Speed • Wrinkliness • Statistical analysis

6. IBM Thinkpad Transnote

7. Database Construction • Record format for the handwriting samples • ID of subject • online or offline • ID of copied subject • word written • first/second/third try • sampling rate (online) or resolution (offline) • file extension

8. <File> xxxx ON OFF yyyy - April T Rate Resolution . Extension Subject ID online offline ID of copied subject word written first try second try third try 100 Hz 300 dpi 600 dpi file extension

9. Handwriting Samples

10. Feature Extraction • Speed • Wrinkliness

11. Speed • The digitizer records the x-y coordinates of the pen movement at a sampling rate of 100Hz • This information is used to calculate the average speed of each handwriting sample

12. Speed The original file of the points • ** Page 10 has 4 scribbles: PageSize is 21.59 cm wide by 27.94 cm high. Scribble 0: time 2002/12/11 23:37 Stroke has 93 points: Point ( 4.73 , 5.02 Point ( 4.73 , 5 ) Point ( 4.73 , 4.99 ) Point ( 4.73 , 4.97 ) .... Scribble 1: time 2002/12/11 23:37 Stroke has 113 points: Point ( 5.82 , 5.26 ) Point ( 5.83 , 5.26 ) Point ( 5.85 , 5.25 ) Point ( 5.88 , 5.24 )... Scribble 2: time 2002/12/11 23:37 Stroke has 7 points: Point ( 7.93 , 4.61 ) Point ( 7.94 , 4.61 ) Point ( 7.96 , 4.61 ) Point ( 7.99 , 4.62 )... Scribble 3: time 2002/12/11 23:37 Stroke has 47 points: Point ( 8.26 , 5.75 ) Point ( 8.27 , 5.75 )....

13. Wrinkliness Wrinkliness = log( high_resolution / low_resolution) / log(2) • high_resolution – the number of pixels on the boundary of the high resolution handwriting sample • low_resolution – the number of pixels on the boundary of the low resolution handwriting sample • Note that the wrinkliness of a straight line = 1.0

14. Original handwriting sample

15. Find the edge of the handwriting

16. Edges of 300 and 600 dpi

17. Number of pixels on the boundary • Convert the scanned images to color images • Count the number of pixels whose (Red < 50, Green < 50, Blue < 50) in two different resolutions • Get the wrinkliness value

18. Sample Results Filename300dpi 600dpi Wrinkliness Speed 0101T1 14894 30583 1.03799867 0.11396973 0101T2 8786 18638 1.084968652 0.107457204 0101T3 9258 19764 1.094102493 0.118184103 0202T1 6453 13765 1.092962679 0.093275242 0202T2 6212 13319 1.100356033 0.094080635 0202T3 5824 12722 1.127243231 0.087968122

19. Information of the ten subjects

20. Summary of handwriting samples • 10 subjects • Each subject wrote • 3 authentic handwriting samples • 3 forgeries of each of the other 9 subjects • Total 300 handwriting samples • 30 authentic • 270 forgeries • Total 900 database records • One online and two resolutions offline for each handwriting sample

21. Speed Hypothesis Test • H0(null hypothesis): the mean speed for the authentic and forged handwritings are about equal • Ha (alternate hypothesis): the mean speed of the authentic handwriting is greater than that of the forged

22. Mean equality test output

23. Reject the null hypothesis • Alpha (level of significance) = 0.05 • p (probability) value is 5.90E-09 which is much less than alpha Reject null hypothesis with a 95% confidence interval Successfully prove the hypothesis

24. Wrinkliness Hypothesis Test • H0 (null hypothesis): log2 ( 600dpif / 300dpif) ~ log2 ( 600dpia/ 300dpia) • Ha (alternative hypothesis): the mean wrinkliness of the authentic handwriting is less than the mean wrinkliness of the forged handwriting

25. Mean equality test output

26. Accept the null hypothesis • Alpha (level of significance) = 0.05 • p (probability) value is 0.065 which is greater than alpha Accept null hypothesis with 95% confidence interval Fail to prove the hypothesis

27. The first possible reason for failure Different writing styles among the three tries of the authentic handwriting First try Second try Third try

28. The second possible reason for failure • Some subjects didn’t forge other subjects’ handwritings carefully Authentic Forged

29. Revised hypothesis test • Eliminate the different authentic writing styles and the poorly forged handwriting samples • Run the hypothesis test again

30. Mean equality test output

31. Reject the null hypothesis • Alpha (level of significance) = 0.05 • p (probability) value is 0.0205 which is less than alpha Reject null hypothesis with 95% confidence interval Successfully prove the hypothesis

32. Conclusion • The average writing speed of the forged handwritings tends to be slower than the speed of the authentic handwritings • “Good” (well formed) forged handwritings tend to be wrinklier (less smooth) than authentic ones

33. Future Extensions • Redo the study using signatures rather than arbitrary words since writing signatures is a highly learned automatic process • Investigate using different resolutions to improve the estimate of wrinkliness • Devise pattern recognition algorithms to filter out the “bad” forged samples automatically • Compute features over portions of the writing rather than over the whole word or signature

34. The End