Abstract
Criminals are always motivated to counterfeit currency notes, especially higher denomination notes. Low-quality counterfeits are created using high-resolution scanners and printers whereas high-quality counterfeits are created using sophisticated currency printing presses and raw materials, often with the assistance of hostile nation states. Identifying counterfeit currency notes is a challenging problem that is hindered by the absence of a publicly-available database of genuine and counterfeit currency notes due to legal constraints. On November 8, 2016, the Government of India declared all 500 and 1,000 denomination notes of the Mahatma Gandhi Series as invalid tender. This research was able to collect and investigate genuine and counterfeit versions of the demonetized notes. Several new security features in the demonetized currency notes were identified and a database of microscope and scanner images has been created for forensic research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Abbasi, A review of different currency recognition systems for Bangladesh, India, China and Euro currency, Research Journal of Applied Sciences, Engineering and Technology, vol. 7(8), pp. 1688–1690, 2014.
A. Ahmadi, S. Omatu, T. Fujinaka and T. Kosaka, Improvement of reliability in banknote classification using reject option and local PCA, Information Sciences, vol. 168(1-4), pp. 277–293, 2004.
A. Ali and M. Manzoor, Recognition system for Pakistani paper currency, Research Journal of Applied Sciences, Engineering and Technology, vol. 6(16), pp. 3078–3085, 2013.
R. Bhavani and A. Karthikeyan, A novel method for counterfeit banknote detection, International Journal of Computer Sciences and Engineering, vol. 2(4), pp. 165–167, 2014.
M. Bozicevic, A. Gajovic and I. Zjakic, Identifying a common origin of toner-printed counterfeit banknotes by micro-Raman spectroscopy, Forensic Science International, vol. 223(1-3), pp. 314–320, 2012.
E. Choi, J. Lee and J. Yoon, Feature extraction for bank note classification using wavelet transforms, Proceedings of the Eighteenth International Conference on Pattern Recognition, pp. 934–937, 2006.
L. Cozzella, C. Simonetti and G. Spagnolo, Is it possible to use biometric techniques as authentication solutions for objects? Biometry vs. hylemetry, Proceedings of the Fifth International Symposium on Communications, Control and Signal Processing, 2012.
A. Frosini, M. Gori and P. Priami, A neural-network-based model for paper currency recognition and verification, IEEE Transactions on Neural Networks, vol. 7(6), pp. 1482–1490, 1996.
S. Gai, G. Yang and W. Minghua, Employing quaternion wavelet transform for banknote classification, Neurocompuing, vol. 118, pp. 171–178, 2013.
H. Hassanpour, A. Yaseri and G. Ardeshiri, Feature extraction for paper currency recognition, Proceedings of the Ninth International Symposium on Signal Processing and its Applications, 2007.
V. Jain and R. Vijay, Indian currency denomination identification using an image processing technique, International Journal of Computer Science and Information Technologies, vol. 4(1), pp. 126–128, 2013.
W. Lee, H. Jang, K. Oh and J. Yu, Design of chipless tag with electromagnetic code for paper-based banknote classification, Proceedings of the Asia-Pacific Microwave Conference, pp. 1406–1409, 2011.
S. Mishra, Pakistan finds a way to counterfeit “high security” Rs. 2,000 currency notes; 11 out of 17 security features copied in fake currency, India.Com, February 13, 2017.
J. Ok, C. Lee, E. Choi and Y. Baek, Fast country classification of banknotes, Proceedings of the Fourth International Conference on Intelligent Systems, Modeling and Simulation, pp. 234–236, 2013.
M. Rahmadhony, S. Wasista and E. Purwantini, Validity currency detector with optical sensor using backpropagation, Proceedings of the International Electronics Symposium, pp. 257–262, 2015.
Reserve Bank of India, Annual Report 2020-2021, Mumbai, India (www.rbi.org.in/scripts/AnnualReportPublications.aspx?Id=1181), 2021.
A. Roy, B. Halder and U. Garain, Authentication of currency notes through printing technique verification, Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, pp. 383–390, 2010.
A. Roy, B. Halder, U. Garain and D. Doermann, Machine-assisted authentication of paper currency: An experiment on Indian bank- notes, International Journal on Document Analysis and Recognition, vol. 18(3), pp. 271–285, 2015.
N. Semary, S. Fadl, M. Essa and A. Gad, Currency recognition system for the visually impaired: Egyptian banknotes as a case study, Proceedings of the Fifth International Conference on Information and Communications Technology and Accessibility, 2015.
J. Xie, C. Qin, T. Liu, Y. He and M. Xu, A new method to identify the authenticity of banknotes based on texture roughness, Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 1268–1271, 2009.
W. Yan and J. Chambers, An empirical approach for digital currency forensics, Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 2988–2991, 2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Chhabra, S., Gupta, G., Gupta, G., Gupta, M. (2021). INDIAN CURRENCY DATABASE FOR FORENSIC RESEARCH. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XVII. DigitalForensics 2021. IFIP Advances in Information and Communication Technology, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-030-88381-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-88381-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88380-5
Online ISBN: 978-3-030-88381-2
eBook Packages: Computer ScienceComputer Science (R0)