Abstract
The paper itself forms an important security feature for many security paper documents. This work attempts to develop a machine assisted tool for authenticating the paper of a security document. Image processing and pattern recognition principles form the basis of this automatic method. Paper pulps play a crucial role in characterizing a paper material. These pulps are visible in the UV scanned image of the document. Therefore, the pulps are first identified in the UV scanned image. This identification is done by borrowing ideas from rice grain detection method. Once the pulps are detected, shape and color features are extracted from them. Paper pulps coming from fake documents are significantly different from those of genuine documents in their shapes and colors. Using the shape and color features, a multilayer back propagation neural network is used to discriminate paper pulps as genuine or fake. The proposed method is tested with Indian banknote samples. Experiment shows that consideration of paper pulps is one of the crucial tests for authenticating paper money.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vila, A., Ferrer, N., Mantecon, J., Breton, D., Garcia, J.F.: Development of a fast and non-destructive procedure for characterizing and distinguishing original and fake Euro notes. Analytica Chimica Acta 559(2), 257–263 (2006)
Counterfeit Currency in Canada, Publication of Royal Canadian Mounted Police (December 2007)
Kaushal, R.: Fake money circulation boosts black economy, India Today (August 5, 2009)
Counterfeit Banknotes, Report of the parliamentary office of science and technology, UK (1996), http://www.parliament.uk/briefing-papers/POST-PN-77.pdf
Williams, M.M., Anderson, R.G.: Currency Design in the United States and abroad: Counterfeit Deterrence and Visual Accessibility. Federal Reserve Bank of St. Louis Review 89(5), 371–414 (2007)
Counterfeit deterrence: Currency design in India and Abroad, The Holography Times 3(6) (2009)
Reserve Bank of India,High Level RBI Group Suggests Steps to Check Menace of Fake Notes, Press release number 2009-2010/232 (August 11, 2009)
Department of Financial Services, Ministry of Finance, Govt. of India, Counterfeit Notes from ATM, Press release number F No 11/16/2011-FI, 3rd Floor, Jeevan Deep Building, New Delhi, India (July 6, 2012)
Mondardini and Massimo, Device for validating banknotes, EPO Patent, No. EP 0537513 (A1) (April 21, 1993)
Graves, B.T., Jones, W.J., Mennie, D.U., Sculits, F.M.: Method and Apparatus for Authenticating and Discriminating Currency, US Patent, No. 5,960,103 (September 28, 1999)
Slepyan, E., Kugel, A., Eisenberg, J.: Currency Verification, US Patent, No. 6,766,045 (July 20, 2004)
Yoshida, K., Kamruzzaman, M., Jewel, F.A., Sajal, R.F.: Design and implementation of a machine vision based but low cost stand-alone system for real time counterfeit Bangladeshi bank notes detection. In: Proc. 10th Int. Conf. on Computer and Information Technology (ICCIT), Dhaka, pp. 1–5 (December 2007)
Roy, A., Halder, B., Garain, U.: Authentication of Currency Notes through Printing Technique Verification. In: Indian. Conf. On Computer Vision, Graphics & Image Processing (ICVGIP), India (2010)
Roy, A., Halder, B., Garain, U., Doermann, D.: Automatic Authentication of Indian Banknotes. In: Machine Vision and Applications (MVA). Springer (2013) ( submitted)
Chia, T.H., Levene, M.J.: Detection of counterfeit U.S. paper money using intrinsic fluoresecence lifetime. Optics Express 17(24) (2009)
Van Renesse, R.L.: Paper based document security - a review. In: IEEE European Conference on Security and Detection, pp. 75–80 (April 1997)
Halder, B., Darbar, R., Garain, U.: Investigating the role of fluorescent paper pulp for detecting counterfeit Banknotes. In: Proc. 5th Int. Workshop on Computational Forensics (IWCF), Tsukuba, Japan (2012)
Sun, C., Berman, M., Coward, D., Osborne, B.: Thickness measurement and crease detection of wheat grains using stereo vision. Pattern Recognition Letters 28(12), 1501–1508 (2007)
Hobson, D.M., Carter, R.M., Yan, Y.: Characterisation and Identification of Rice Grains through Digital image Analysis. In: Proc. IEEE Instrumentation and Measurement Technology Conference (IMTC), Poland, pp. 1–5 (2007)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Systems, Man and Cybernetics (SMC) 3(6), 610–621 (1973)
BARS 5000 Currency sorter, by BARS GmbH, Siemensstr. 14, D-840 30 Landshut, Germany
Garg, G., Sharma, P.K., Chaudhury, S.: Image based document authentication using DCT. Pattern Recognition Letters 22, 725–729 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Halder, B., Darbar, R., Garain, U., Mondal, A.C. (2014). Analysis of Fluorescent Paper Pulps for Detecting Counterfeit Indian Paper Money. In: Prakash, A., Shyamasundar, R. (eds) Information Systems Security. ICISS 2014. Lecture Notes in Computer Science, vol 8880. Springer, Cham. https://doi.org/10.1007/978-3-319-13841-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-13841-1_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13840-4
Online ISBN: 978-3-319-13841-1
eBook Packages: Computer ScienceComputer Science (R0)