Download - Forged Handwriting Detection
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Forged Handwriting DetectionForged Handwriting Detection
Hung-Chun ChenM.S. Thesis in Computer ScienceAdvisors: Drs. Cha and Tappert
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MotivationMotivation
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
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Key IdeaKey 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
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HypothesesHypotheses
• 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
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MethodologyMethodology
Handwriting sample collectionMeasurement (feature) extraction– Speed–Wrinkliness
Statistical analysis
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IBM Thinkpad TransnoteIBM Thinkpad Transnote
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Database ConstructionDatabase Construction
Record format for the handwriting samples1. ID of subject
2. online or offline
3. ID of copied subject
4. word written
5. first/second/third try
6. sampling rate (online) or resolution (offline)
7. file extension
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Subject ID
<File>Rate
ResolutionExtension.TApril-yyyy
ONOFF
xxxx
onlineoffline
ID of copied subject
word written
first trysecond trythird try
100 Hz300 dpi600 dpi file
extension
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Handwriting SamplesHandwriting Samples
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Feature ExtractionFeature Extraction
Speed
Wrinkliness
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SpeedSpeed
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
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SpeedSpeed
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 )....
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WrinklinessWrinkliness
WrinklinessWrinkliness = log( = log( high_resolution high_resolution / / low_resolutionlow_resolution) / log(2)) / 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
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Original handwriting sampleOriginal handwriting sample
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Find the edge of the handwriting Find the edge of the handwriting
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Edges of 300 and 600 dpiEdges of 300 and 600 dpi
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Number of pixels on the boundaryNumber 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
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Sample ResultsSample Results
Filename 300dpi 600dpi Wrinkliness Speed0101T1 14894 30583 1.03799867 0.113969730101T2 8786 18638 1.084968652 0.1074572040101T3 9258 19764 1.094102493 0.1181841030202T1 6453 13765 1.092962679 0.0932752420202T2 6212 13319 1.100356033 0.0940806350202T3 5824 12722 1.127243231 0.087968122
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Information of the ten subjectsInformation of the ten subjects
UserID Age Ethnicity Education Gender Schooling Handiness
1 30 Caucasian Master F English R
2 30 Asian Master F Foreign R
3 20 Asian Bachelor F Foreign R
4 27 Asian Master M Foreign R
5 28 Asian Master F Foreign R
6 35 Caucasian Bachelor M English R
7 60 Caucasian Master M English R
8 67 Asian Beyond H.S F Foreign L
9 35 Caucasian PHD F English R
10 70 Asian Beyond H.S M Foreign L
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Summary of handwriting samplesSummary 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
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Speed Hypothesis TestSpeed 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
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Mean equality test outputMean equality test output
Alpha (level of significance) = 5%
Authentic Forged
Mean 0.083 0.057
Variance 0.00050 0.00053
Observations na=30 nf=270
Pooled Variance 0.00053
Hypothesized Mean Difference 0
df 298
t Stat 5.87
P(T<=t) one-tail 5.90E-09t Critical one-tail 1.65
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Reject the null hypothesisReject the null hypothesis
Alpha (level of significance) = 0.05p (probability) value is 5.90E-09
which is much less than alpha
Successfully prove the hypothesis
Reject null hypothesis with a 95% confidence interval
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Wrinkliness Hypothesis TestWrinkliness 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
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Mean equality test outputMean 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 0.0013
Hypothesized Mean Difference 0
df 298
t Stat 1.52
P(T<=t) one-tail 0.065t Critical one-tail 1.65
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Accept the null hypothesisAccept the null hypothesis
Alpha (level of significance) = 0.05p (probability) value is 0.065
which is greater than alpha
Fail to prove the hypothesis
Accept null hypothesis with 95% confidence interval
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The first possible reason for failureThe first possible reason for failure
Different writing styles among the three tries of the authentic handwriting
First try Second try Third try
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The second possible reason for failureThe second possible reason for failure
Some subjects didn’t forge other subjects’ handwritings carefully
Authentic Forged
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Revised hypothesis testRevised hypothesis test
Eliminate the different authentic writing styles and the poorly forged handwriting samples
Run the hypothesis test again
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Mean equality test outputMean 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 0
df 211
t Stat 2.06
P(T<=t) one-tail 0.0205t Critical one-tail 1.65
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Reject the null hypothesisReject the null hypothesis
Alpha (level of significance) = 0.05p (probability) value is 0.0205 which is less than alpha
Successfully prove the hypothesis
Reject null hypothesis with 95% confidence interval
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ConclusionConclusion
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
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Future ExtensionsFuture 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
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The EndThe End