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Identification of Fake Printed Medicine Packaging from a SVM Approach and Dots Shape Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 8 |
| Year of Publication: 2023 |
| Authors: Indrama Das, Swati Bandyopadhyay, Alain Trémeau |
10.5120/ijca2023922730
|
Indrama Das, Swati Bandyopadhyay, Alain Trémeau . Identification of Fake Printed Medicine Packaging from a SVM Approach and Dots Shape Features. International Journal of Computer Applications. 185, 8 ( May 2023), 5-12. DOI=10.5120/ijca2023922730
Abstract
Medicine package printing becomes critical nowadays as fake medicines can be easily packed and sold if printed with the proper color combination. Hence, it is important to identify whether a printed package is printed by original medicine manufacturers or their authorized printers or it is printed by counterfeiters. Scanning or photographing the package and reprinting it is one approach for forging an authentic package sample. In this work, the microscopic examination of printed foils has been carried out to verify whether it is original or not. Blister foil is widely used in the pharmaceutical packaging sector and hence it is chosen as the substrate. The microscopic dot pattern can be viewed as a distinct signature of printing processes. In this study, the reference target chart IT 8.7/3 is printed using three different gravure printers (P1, P2, P3). The images of the print samples are then captured using camera. The images are printed again in the same three printers and then the samples are named as reprint samples (R1, R2, R3). Different shape descriptors index parameters such as dot area, perimeter, circularity, eccentricity, solidity, major and minor axis of dots have been used for classification between the print sample and scanned reprint sample. The study demonstrated that print and reprint dot shape descriptor index parameters might be utilized to distinguish at a microscopic scale. A multi-classification-based method Support Vector Machine have been utilized using shape descriptor index features for print and reprint source identification. The suggested method successfully classified the print and reprint samples at different dot percentages with a high rate of accuracy.
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