Authentication of a Gravure Printer from Color Values using an Artificial Neural Network

International audienceCounterfeited packaging products (Pharmaceuticals) can create severe health hazard. While counterfeiting the pharmaceutical product, printing and packaging takes very crucial role as the customer buy the product by the attraction and information provided by the package. So the counterfeiters emphasize more on packages and associated printing. This work is focused on pharmaceutical package printing not only for its social implication but also for its technological variation. Since most of the medicines are packed in metal foil, the printing technology associated is gravure printing. Little work has been done on this technology and its security aspects. The common ways to counterfeit the packages are to copy the text and images of the package and to reproduce it. However, the variation of color while reprinting it may be assessed to check whether the printing is done by the original manufacturer or their authenticated printer house. Scanning or taking photographs of the package and re-printing is one of the methods to counterfeit the original package sample. Different digital camera, mobile camera, scanner etc have been used to scanned the original sample and then to reprint it. When the image of the original print is taken through different mobiles, camera or scanners, the color values are not the same as the original print even if it is printed on the same printer. However, when the scanned samples are reprinted with different printers, the differences are much higher in comparison to the original print. In this study, blister foil has been taken as substrate and a reference color chart (IT8.7/3) printed with 4-color gravure printing machine. The reference image has been printed with three different gravure printers (P1, P2 ,P3). Then the images of print samples are taken by different input devices. The images are then printed in those three printers again. Study the difference in Lightness and color differences are analyzed to assess the difference of print and reprint samples among different gravure printing press. Artificial Neural Network (ANN) model is used to predict the CIELAB color values of a print sample printed from a printer. In this study, 70% of color patches (total 928) have been used to train the network, 15% of the data used as cross-validation and 15% of the data is used to verify the accuracy of the network. Then the predicted color values of one printer are compared with other print and scanned reprint sample to assess the differences. It has been observed that the difference of predicted color values with the print samples are much less. The difference becomes much higher for scanned reprint samples. Hence it will be possible to identify the fake sample (if someone tries to reprint it after scanning or taking images of original multicolor artwork and reprints it). Hence the predicted difference could be used to protect medicine packaging from counterfeiting

Analysis of Spectral differences between Printers to detect the Counterfeit Medicine Packaging

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Study on Color Gamut to Authenticate the Gravure Printers Output

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