Forged Handwriting Detection Hung-Chun Chen M.S. Thesis 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 Important to develop an objective system to identify forged handwriting, or at least to identify those handwritings that are likely to be forged

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

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

Methodology Handwriting sample collection Measurement (feature) extraction –Speed –Wrinkliness Statistical analysis

IBM Thinkpad Transnote

Database Construction Record format for the handwriting samples 1.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

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

Handwriting Samples

Feature Extraction Speed Wrinkliness

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

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 )....

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

Original handwriting sample

Find the edge of the handwriting

Edges of 300 and 600 dpi

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

Sample Results Filename 300dpi 600dpi Wrinkliness Speed 0101T T T T T T

Information of the ten subjects UserIDAgeEthnicityEducationGenderSchoolingHandiness 130CaucasianMasterFEnglishR 230AsianMasterFForeignR 320AsianBachelorFForeignR 427AsianMasterMForeignR 528AsianMasterFForeignR 635CaucasianBachelorMEnglishR 760CaucasianMasterMEnglishR 867AsianBeyond H.SFForeignL 935CaucasianPHDFEnglishR 1070AsianBeyond H.SMForeignL

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

Speed Hypothesis Test H 0 (null hypothesis): the mean speed for the authentic and forged handwritings are about equal H a (alternate hypothesis): the mean speed of the authentic handwriting is greater than that of the forged

Mean equality test output Alpha (level of significance) = 5% AuthenticForged Mean Variance Observationsn a= 30n f =270 Pooled Variance Hypothesized Mean Difference0 df298 t Stat5.87 P(T<=t) one-tail 5.90E-09 t Critical one-tail1.65

Reject the null hypothesis Alpha (level of significance) = 0.05 p (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 Test H 0 (null hypothesis): log 2 ( 600dpi f / 300dpi f ) ~ log 2 ( 600dpi a / 300dpi a ) H a (alternative hypothesis): the mean wrinkliness of the authentic handwriting is less than the mean wrinkliness of the forged handwriting

Mean equality test output Alpha (level of significance) = 5% ForgedAuthentic Mean Variance Observations27030 Pooled Variance Hypothesized Mean Difference0 df298 t Stat1.52 P(T<=t) one-tail t Critical one-tail1.65

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

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

The second possible reason for failure Some subjects didn’t forge other subjects’ handwritings carefully AuthenticForged

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

Mean equality test output Alpha (level of significance) = 5% ForgedAuthentic Mean Variance Observations19023 Pooled Variance Hypothesized Mean Difference0 df211 t Stat2.06 P(T<=t) one-tail t Critical one-tail1.65

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

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

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

The End