Data-Driven Strategies for Improving Global Counterfeit Currency Surveillance: A Big Data Perspective
DOI:
https://doi.org/10.51699/ijbde.v3i2.3471Keywords:
Counterfeit Currency Detection, Data Science in Finance, Machine Learning Algorithms, Blockchain Technology, Financial Fraud SurveillanceAbstract
The study delves into the application of data science and emerging technologies in the detection and surveillance of counterfeit currency, a burgeoning challenge with significant implications for the global financial system. With counterfeiters employing increasingly sophisticated methods to circumvent traditional detection mechanisms, this research emphasizes the integration of advanced data analytics, machine learning models, and pattern recognition algorithms to enhance the efficacy of counterfeit detection operations. Utilizing a comprehensive dataset derived from financial transactions, law enforcement reports, and social media analytics, the study showcases the superiority of data-driven approaches over conventional methods in identifying and mitigating the circulation of counterfeit notes. Through a detailed examination of the methodologies employed, including data preprocessing and the application of machine learning techniques, this research highlights key findings that demonstrate the potential of technologies such as blockchain and AI in revolutionizing the fight against counterfeit currency. The study also discusses the implications of these findings for policymakers, financial institutions, and law enforcement agencies, underscoring the importance of collaboration, technological innovation, and the exploration of new data sources. Furthermore, the research addresses the challenges and limitations encountered, including data accessibility and ethical considerations, while proposing areas for future investigation to overcome these hurdles and advance the field of counterfeit currency detection. This study contributes to the development of more sophisticated, efficient, and adaptive surveillance and detection systems, offering a promising outlook for enhancing the integrity of global financial systems in the face of evolving counterfeiting threats.
References
C. G. Pachón, D. M. Ballesteros, and D. Renza, “Fake banknote recognition using deep learning,” Applied Sciences, 2021, [Online]. Available: https://www.mdpi.com/2076-3417/11/3/1281
A. Bruna, G. M. Farinella, G. C. Guarnera, and S. Battiato, “Forgery detection and value identification of Euro banknotes,” Sensors, 2013, [Online]. Available: https://www.mdpi.com/1424-8220/13/2/2515
A. Burger, “The devil’s workshop: a memoir of the Nazi counterfeiting operation,” (No Title), 2009, [Online]. Available: https://cir.nii.ac.jp/crid/1130000795071568512
T. D. Pham, C. Park, D. T. Nguyen, G. Batchuluun, and ..., “Deep learning-based fake-banknote detection for the visually impaired people using visible-light images captured by smartphone cameras,” IEEE …, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9050503/
J. Chambers, W. Yan, A. Garhwal, and ..., “Currency security and forensics: a survey,” Multimedia Tools and …, 2015, doi: 10.1007/s11042-013-1809-x.
A. Guedes, M. Algarra, A. C. Prieto, B. Valentim, and ..., “Raman microspectroscopy of genuine and fake euro banknotes,” Spectroscopy …, 2013, doi: 10.1080/00387010.2013.769007.
A. Vila, N. Ferrer, J. Mantecon, D. Breton, and J. F. Garcia, “Development of a fast and non-destructive procedure for characterizing and distinguishing original and fake euro notes,” Anal Chim Acta, 2006, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0003267005019732
Z. Wang, D. Lu, D. Zhang, M. Sun, and Y. Zhou, “Fake modern Chinese painting identification based on spectral–spatial feature fusion on hyperspectral image,” Multidimensional Systems and …, 2016, doi: 10.1007/s11045-016-0429-9.
W. J. Choi, G. H. Min, B. H. Lee, J. H. Eom, and ..., “Counterfeit detection using characterization of safety feature on banknote with full-field optical coherence tomography,” Journal of the Optical …, 2010, [Online]. Available: https://opg.optica.org/abstract.cfm?uri=JOSK-14-4-316
K. Sathisha, “Bank automation system for Indian currency-a novel approach,” 2011 IEEE Recent Advances in Intelligent …, 2011, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6069322/
K. K. Debnath, S. U. Ahmed, M. Shahjahan, and K. Murase, “A paper currency recognition system using negatively correlated neural network ensemble,” J Multimed, 2010, [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=5c00c352f53667eb095d56e5a3147ef3c59086f3#page=22
H. Gou, X. Li, X. Li, and J. Yi, “A reliable classification method for paper currency based on LVQ neural network,” Advances in Computer Science and Education …, 2011, doi: 10.1007/978-3-642-22456-0_35.
E. J. Green and W. E. Weber, “Will the new 100 bill decrease counterfeiting,” … Quarterly Review, 1996, [Online]. Available: https://econwpa.ub.uni-muenchen.de/econ-wp/mac/papers/9609/9609003.pdf
F. Grijalva, J. C. Rodriguez, J. Larco, and ..., “Smartphone recognition of the US banknotes’ denomination, for visually impaired people,” 2010 IEEE …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5631773/
H. Hassanpour and P. M. Farahabadi, “Using Hidden Markov Models for paper currency recognition,” Expert Syst Appl, 2009, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417409000463
L. Jing and M. Jin, “About RMB number identification with genetic evolution neural network,” 2010 International Conference on Computer …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5610492/
M. Jadhav, Y. kumar Sharma, and ..., “Currency identification and forged banknote detection using deep learning,” … on innovative trends …, 2019, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9170225/
L. Liu, Y. Ye, Y. Xie, and L. Pu, “Serial number extracting and recognizing applied in paper currency sorting system based on RBF Network,” 2010 international conference on …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5677049/
S. Omatu, M. Yoshioka, and Y. Kosaka, “Reliable banknote classification using neural networks,” 2009 Third International …, 2009, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5359628/
B. Singh, P. Badoni, and K. Verma, “Computer vision based currency classification system,” Int J Comput Appl, 2011, [Online]. Available: https://www.academia.edu/download/31132825/10.1.1.206.3102.pdf
L. Wenhong, T. Wenjuan, C. Xiyan, and ..., “Application of support vector machine (SVM) on serial number identification of RMB,” 2010 8th World …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5554382/
W. Clarkson, T. Weyrich, A. Finkelstein, and ..., “Fingerprinting blank paper using commodity scanners,” 2009 30th IEEE …, 2009, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5207652/
B. Q. Cao and J. X. Liu, “Currency recognition modeling research based on BP neural network improved by gene algorithm,” 2010 second international conference on …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5421085/
C. L. Tsai, A. Mukundan, C. S. Chung, Y. H. Chen, Y. K. Wang, and ..., “Hyperspectral imaging combined with artificial intelligence in the early detection of esophageal cancer,” Cancers (Basel), 2021, [Online]. Available: https://www.mdpi.com/2072-6694/13/18/4593
Y. J. Fang, A. Mukundan, Y. M. Tsao, C. W. Huang, and ..., “Identification of early esophageal cancer by semantic segmentation,” Journal of Personalized …, 2022, [Online]. Available: https://www.mdpi.com/2075-4426/12/8/1204
M. Han and J. Kim, “Joint banknote recognition and counterfeit detection using explainable artificial intelligence,” Sensors, 2019, [Online]. Available: https://www.mdpi.com/1424-8220/19/16/3607
L. Nandanwar, P. Shivakumara, U. Pal, T. Lu, and ..., “A new method for detecting altered text in document images,” … Journal of Pattern …, 2021, doi: 10.1142/S0218001421600107.
Z. Li, X. Zhou, and Y. Chen, “Research for the intelligent RMB sorter based on ANN,” 2009 9th International Conference on …, 2009, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5274513/
A. U. Islam, M. J. Khan, M. Asad, H. A. Khan, and K. Khurshid, “iVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis,” Data Brief, 2022, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352340922001755
D. X. Wang and J. K. Teng, “Research and analysis of electronic cash payment system,” 2010 International Conference on …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5608359/
P. Kumpulainen, M. Mettänen, M. Lauri, and ..., “Relating halftone dot quality to paper surface topography,” Neural Computing and …, 2011, doi: 10.1007/s00521-010-0497-y.
A. Trémeau and D. Muselet, “Recent trends in color image watermarking,” J Imaging Sci Technol, 2009, [Online]. Available: https://library.imaging.org/admin/apis/public/api/ist/website/downloadArticle/jist/53/1/art00002
R. Thakur and R. Rohilla, “Recent advances in digital image manipulation detection techniques: A brief review,” Forensic Sci Int, 2020, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0379073820301730
S. Y. Huang, A. Mukundan, Y. M. Tsao, Y. Kim, F. C. Lin, and ..., “Recent advances in counterfeit art, document, photo, hologram, and currency detection using hyperspectral imaging,” Sensors, 2022, [Online]. Available: https://www.mdpi.com/1424-8220/22/19/7308
N. G. Shankar, N. Ravi, and Z. W. Zhong, “A real-time print-defect detection system for web offset printing,” Measurement, 2009, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0263224108001838
L. Heudt, D. Debois, T. A. Zimmerman, L. Köhler, and ..., “Raman spectroscopy and laser desorption mass spectrometry for minimal destructive forensic analysis of black and color inkjet printed documents,” Forensic science …, 2012, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0379073811005925
M. Vohland, M. Ludwig, S. Thiele-Bruhn, and B. Ludwig, “Quantification of soil properties with hyperspectral data: Selecting spectral variables with different methods to improve accuracies and analyze prediction …,” Remote Sens (Basel), 2017, [Online]. Available: https://www.mdpi.com/2072-4292/9/11/1103
V. V Das, “Protocol for anonymous e-cash for secure electronic commerce-initiation,” 2009 Second International Conference on Future …, 2009, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5380917/
J. F. Q. Pereira, C. S. Silva, A. Braz, M. F. Pimentel, and ..., “Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents,” Microchemical …, 2017, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0026265X1630131X
A. V Malviya and S. A. Ladhake, “Pixel based image forensic technique for copy-move forgery detection using auto color correlogram,” Procedia Comput Sci, 2016, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050916001812
H. Dasari and C. Bhagvati, “Identification of non-black inks using HSV colour space,” Ninth international conference on …, 2007, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/4378757/
J. van Beusekom and F. Shafait, “Distortion measurement for automatic document verification,” 2011 International Conference on …, 2011, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6065321/
T. H. Chia and M. J. Levene, “Detection of counterfeit US paper money using intrinsic fluorescence lifetime,” Opt Express, 2009, [Online]. Available: https://opg.optica.org/abstract.cfm?uri=oe-17-24-22054
M. J. Khan, A. Yousaf, A. Abbas, and ..., “Deep learning for automated forgery detection in hyperspectral document images,” Journal of Electronic …, 2018, doi: 10.1117/1.JEI.27.5.053001.short.
H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and ..., “Cancer detection using infrared hyperspectral imaging,” Cancer Sci, 2011, doi: 10.1111/j.1349-7006.2011.01849.x.
J. Sun, Y. Cao, X. Zhou, M. Wu, Y. Sun, and ..., “Detection for lead pollution level of lettuce leaves based on deep belief network combined with hyperspectral image technology,” Journal of Food …, 2021, doi: 10.1111/jfs.12866.
C. Park, S. W. Cho, N. R. Baek, J. Choi, and K. R. Park, “Deep feature-based three-stage detection of banknotes and coins for assisting visually impaired people,” IEEE Access, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9217508/
M. Shahjahan, “A currency recognition system using negatively correlated neural network ensemble,” 2009 12th International Conference on …, 2009, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5407265/
J. Guo, Y. Zhao, and A. Cai, “A reliable method for paper currency recognition based on LBP,” 2010 2nd IEEE InternationalConference …, 2010, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5657978/
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