forged handwriting detection
DESCRIPTION
Forged Handwriting Detection. Hung-Chun Chen M.S. in Computer Science Advisors: Drs. Cha and Tappert. Motivation. Important documents require signatures to verify the identity of the writer Experts are required to differentiate between authentic and forged signatures - PowerPoint PPT PresentationTRANSCRIPT
Forged Handwriting DetectionForged Handwriting Detection
Hung-Chun ChenM.S. in Computer Science
Advisors: Drs. Cha and Tappert
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
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
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
MethodologyMethodology
Handwriting sample collectionFeature extraction– Speed–Wrinkliness
Statistical analysis
IBM Thinkpad TransnoteIBM Thinkpad Transnote
Database ConstructionDatabase Construction
Record format for the handwriting samples1. ID of subject2. online or offline 3. ID of copied subject4. word written 5. first/second/third try 6. sampling rate (online) or resolution (offline)7. file extension
Subject ID
<File>Rate
Resolution Extension.TApril-yyyyONOFFxxxx
onlineoffline
ID of copied subject
word written
first trysecond trythird try
100 Hz300 dpi600 dpi file
extension
Handwriting SamplesHandwriting Samples
Feature ExtractionFeature Extraction
Speed
Wrinkliness
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
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 )....
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
Original handwriting sampleOriginal handwriting sample
Find the edge of the handwriting Find the edge of the handwriting
Edges of 300 and 600 dpiEdges of 300 and 600 dpi
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
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
Information of the ten subjectsInformation of the ten subjectsUserID 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
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
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
Mean equality test outputMean equality test outputAlpha (level of significance) = 5%
Authentic ForgedMean 0.083 0.057Variance 0.00050 0.00053
Observations na=30 nf=270
Pooled Variance 0.00053Hypothesized Mean Difference 0df 298t Stat 5.87
P(T<=t) one-tail 5.90E-09t Critical one-tail 1.65
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
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
Mean equality test outputMean equality test output
Alpha (level of significance) = 5%
Forged AuthenticMean 1.094 1.083
Variance 0.0013 0.0010Observations 270 30
Pooled Variance 0.0013Hypothesized Mean Difference 0df 298t Stat 1.52
P(T<=t) one-tail 0.065t Critical one-tail 1.65
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
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
The second possible reason for failureThe second possible reason for failure
Some subjects didn’t forge other subjects’ handwritings carefully
Authentic Forged
Revised hypothesis testRevised hypothesis test
Eliminate the different authentic writing styles and the poorly forged handwriting samples
Run the hypothesis test again
Mean equality test outputMean equality test outputAlpha (level of significance) = 5%
Forged Authentic
Mean 1.097 1.079
Variance 0.0016 0.0009Observations 190 23
Pooled Variance 0.0015Hypothesized Mean Difference 0df 211t Stat 2.06
P(T<=t) one-tail 0.0205t Critical one-tail 1.65
Reject the null hypothesisReject the null hypothesisAlpha (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
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
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
The EndThe End