1. Forged Handwriting Detection
Hung-Chun Chen
M.S. Thesis 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
Measurement (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> Subject ID online offline ID of copied subject
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 cm wide by 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 Filename 300dpi 600dpi Wrinkliness Speed

19. Information of the ten subjects
UserID
Age
Ethnicity
Education
Gender
Schooling
Handiness 1 30
Caucasian
Master F
English R 2
Asian
Foreign 3 20
Bachelor 4 27 M 5 28 6 35 7 60 8 67
Beyond H.S L 9
PHD 10 70

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
Alpha (level of significance) = 5%
Authentic
Forged
Mean
0.083
0.057
Variance
Observations
na=30
nf=270
Pooled Variance
Hypothesized Mean Difference df
298
t Stat
5.87
P(T<=t) one-tail
5.90E-09
t Critical one-tail
1.65

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
Alpha (level of significance) = 5%
Forged
Authentic
Mean
1.094
1.083
Variance
0.0013
0.0010
Observations
270 30
Pooled Variance
Hypothesized Mean Difference df
298
t Stat
1.52
P(T<=t) one-tail
0.065
t Critical one-tail
1.65

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
Alpha (level of significance) = 5%  
Forged
Authentic
Mean
1.097
1.079
Variance
0.0016
0.0009
Observations
190 23
Pooled Variance
0.0015
Hypothesized Mean Difference df
211
t Stat
2.06
P(T<=t) one-tail
0.0205
t Critical one-tail
1.65

31. Reject the null hypothesis
Alpha (level of significance) = 0.05
p (probability) value is
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