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Reseach Article

Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm

by Megha Jadhav, Yogeshkumar Sharma, G. M. Bhandari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 44
Year of Publication: 2020
Authors: Megha Jadhav, Yogeshkumar Sharma, G. M. Bhandari
10.5120/ijca2020919970

Megha Jadhav, Yogeshkumar Sharma, G. M. Bhandari . Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm. International Journal of Computer Applications. 177, 44 ( Mar 2020), 36-40. DOI=10.5120/ijca2020919970

@article{ 10.5120/ijca2020919970,
author = { Megha Jadhav, Yogeshkumar Sharma, G. M. Bhandari },
title = { Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2020 },
volume = { 177 },
number = { 44 },
month = { Mar },
year = { 2020 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number44/31203-2020919970/ },
doi = { 10.5120/ijca2020919970 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:38.652519+05:30
%A Megha Jadhav
%A Yogeshkumar Sharma
%A G. M. Bhandari
%T Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 44
%P 36-40
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a days as many counterfeit banknotes are manufactured and circulated in global market, which results in significant damage and harm to society. Recognizing the currency's originality is a very difficult task to general person. Because of advancement in generating highly accurate fake currency. There are several techniques available, such as automatic sorting of banknotes in payment facilities, automated payment machines or sales machines, which consists of several tasks such as identification of banknote type, classification of recirculation fitness and detection of fake banknotes. Banknote identification is the most important approach, based on an image processing system. There are many techniques used in the classification of banknotes by different countries that has been conducted experiments on separate image data sets of each country. Deep learning is a machine learning technique which analyzes & learns the features of the original note. Using the neural networks, the most important aspect is to find more essential features. In the age of big data, in which vast amounts of data must be processed for any application in the real world, the superior techniques are deep learning. In this study, banknotes from various countries are examined by extracting their minute features in carefully and analyzing them using deep learning. Proposed system recommended a Convolutional Neural Network algorithm to detect Forged banknote using dataset of multiple country currency. This approach is chosen to achieve high accuracy with good performance with respect to loss and accuracy in training and validation in terms of huge dataset. So it helps individuals to avoid personal economic damage caused by counterfeiters.

References
  1. Achal Kamble , Prof. M. S. Nimbarte, “Design and Implementation of Fake Currency Detection System”, International Journal on Future Revolution in Computer Science & Communication Engineering, Volume 4, Issue 4,pp. 400- 405, 2018.
  2. Tushar Agasti et al., “Fake currency detection using image processing”, IOP Conf. Series: Materials Science and Engineering, 2017.
  3. Monali Patil et al., “Fake Currency Detection using Image Processing”, International Journal on Future Revolution in Computer Science & Communication Engineering, ISSN: 2454-4248, Volume: 4 Issue: 4, pp. 865– 868,2018.
  4. Gai, S., et.al.,”Employing quaternion wavelet transform for banknote classification.” Neurocomputing ,2013, 118:171–178
  5. Bhurke, C.; Sirdeshmukh, M.; Kanitkar, M.S. Currency recognition using image processing. Int. J. Innov. Res. Comput. Commun. Eng. ,pp. 4418–4422, 2015.
  6. Chae, et. al , “The Study for Authenticity Distinguish of Bank note using UV Information,” Proceedings of KIIT Summer Conference, pp. 753-756, 2009.
  7. Lee, G. H., and Park, T. H., 2011, “Automatic Extraction of UV patterns for Paper Money Inspection,” Journal of Korean Institute of Intelligent Systems, 21 (3), pp. 365-371.
  8. Syed ejaz ali, " challenges in Indian currency denomination recognition & authentication", international journal of research in engineering and technology, 2014.
  9. Tuyen Danh Pham, et al., “Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors”, 2018.
  10. Krizhevsky, et al.,  "ImageNet classification with deep convolutional neural networks", Communication of the ACM. Pp.84-90. 2017.
  11. Dr. Yogesh Kumar Sharma, “security-in-digital-images-using-visualcryptography-scheme”,Journal of Computational Information Systems, ISSN: 1553-9105, Volume No. 14, Issue No. 6, pp. 49-57, Nov. 2018.
  12. Dr. Yogesh Kumar Sharma, “An Empirical Research Framework for Encryption Authentication with Image and Video” ,Journal of Advance Research in Dynamical and Control Systems, ISSN: 1943- 023X, Volume No. 10, Issue No. 8, pp. 59-71, 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Currency Recognition Currency Identification