1. Detection of Forged Handwriting Using a Fractal Number Estimate of Wrinkliness 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 replicate handwriting shape and size by carefully copying or tracing the authentic handwriting

2. Hypotheses 1. Good forgeries – those that retain the shape and size of authentic writing – tend to be written more slowly and carefully than authentic writing 2. Good forgeries are likely to be wrinklier (less smooth) than authentic handwriting

3. Methodology Collected online handwriting samples and then digitally scanned them 10 subjects: 3 authentic + 3*9 forgery samples The x-y coordinates of the online samples were used to calculate the speed of the handwriting. The samples were digitally scanned at two different resolutions to calculate a fractal number estimate of the wrinkliness of the writing Employed the IBM Transnote, pen-enabled notebook computer and associated SDK

4. Results 1. Writing speed of good forgeries was significantly slower than that of the authentic writings (p = 10 -8 ) 2. Wrinkliness of the good forgeries was significantly greater than that of the authentic writings (p = 0.02) Therefore, it is possible to identify candidate forgeries from scanned documents