Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches

Sezen Bal; Emre Canayaz; Zeynep Beyza Kabataş Soyer

doi:10.28979/jarnas.1942318

Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches

Abstract

Evolving counterfeiting techniques have turned counterfeit banknote detection into a multidimensional problem that can no longer be fully addressed through traditional security measures alone. While artificial intelligence (AI), particularly convolutional neural networks (CNNs), has emerged as a promising solution, its success remains heavily dependent on training data quality. Access to genuine images is restricted by copyright and central bank regulations, forcing a reliance on synthetic data, which often triggers domain shift and reliability issues. This study employs a comparative case analysis, treating the Turkish and international ecosystems as two primary macro-cases evaluated against legal, ethical, technical, and accessibility criteria. By establishing a causal linkage between technical errors and their resulting legal liabilities, the research explicitly differentiates between the criminal and civil consequences of misclassification. The findings indicate that focusing solely on technical accuracy is insufficient. Systemic limitations related to dataset transparency, regulatory compliance, and accessibility implementation represent common weaknesses in both settings. Consequently, the study proposes a lifecycle-anchored governance assessment rubric that aligns technical performance with legal accountability and inclusive design principles, providing a replicable evaluative instrument for the trustworthy deployment of AI in financial security applications.

Keywords

Ethical Statement

Ethics committee approval was not required for this study.

References

Central Bank of the Republic of Türkiye, Banknotes. Accessed 22 June 2026.
Republic of Türkiye, Law No. 5846 on Intellectual and Artistic Works. Accessed 22 June 2026.
World Wide Web Consortium, Web Content Accessibility Guidelines WCAG 2.1 (2018). Accessed 22 June 2026.
United Nations Office of the High Commissioner for Human Rights, Convention on the Rights of Persons with Disabilities. Accessed 22 June 2026.
S. Kaya, Evaluation of the administration’s obligations within the framework of the principles of equality, positive discrimination, and distributive justice, Journal of Administrative Law and Sciences (21) (2023) 85-119.
H. T. Yazıcı, An analysis of disability rights movements in Türkiye, Journal of Sociology Studies 3 (2) (2024) 63–78.
E. Bayram, G. B. Özbek, The use of artificial intelligence-based learning methods in finance: A bibliometric analysis, Çukurova University Journal of Social Sciences 34 (2025) 303–321.
T. D. Pham, Y. W. Lee, C. Park, K. R. Park, Deep learning-based detection of fake multinational banknotes in a cross-dataset environment utilizing smartphone cameras for assisting visually impaired individuals, Mathematics 10 (9) (2022) 1616.

World Trade Organization, Agreement on Trade-Related Aspects of Intellectual Property Rights (1994). Accessed 22 June 2026.
Republic of Türkiye, Law No. 6698 on the Protection of Personal Data (2016). Accessed 22 June 2026.
European Union, Regulation EU 2016/679 General Data Protection Regulation (2016). Accessed 22 June 2026.
European Union, Regulation EU 2024/1689 Artificial Intelligence Act (2024). Accessed 22 June 2026.
F. Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press, Cambridge, 2015.
E. Gill-Pedro, A. Moberg, Law and the governance of artificial intelligence, Yearbook of Socio-Economic Constitutions 2023 (2024) 1–13.
M. Thomas, K. Meehan, Banknote object detection for the visually impaired using a CNN, Proceedings of the 32nd Irish Signals and Systems Conference (ISSC), Athlone, Ireland, 2021, pp. 1–6.
M. Han, J. Kim, Joint banknote recognition and counterfeit detection using explainable artificial intelligence, Sensors 19 (16) (2019) 3607.
S.-Y. Huang, A. Mukundan, Y.-M. Tsao, Y. Kim, F.-C. Lin, H.-C. Wang, Recent advances in counterfeit art, document, photo, hologram, and currency detection using hyperspectral imaging, Sensors 22 (19) (2022) 7308.
P. Saraswathi, V. A. Joanna, A. S. Priya, M. Prabha, Automated detection of counterfeit Indian currency using image processing and machine learning, Proceedings of the 5th International Conference on Advancement in Electronics & Communication Engineering (AECE), Ghaziabad, India, 2025, pp. 588–593.
D. Azamuke, C. K. Bamwesigye, M. Katarahweire, A. J. Kamurasi, J. M. Businge, I. Ssozi, J. P. Mukisa, E. Lule, E. Bainomugisha, DeepFakesUG: Detecting counterfeit Ugandan banknotes using deep learning, in: T. Girma Debelee, A. Ibenthal, F. Schwenker, Y. M. Ayano (Eds.), Pan-African Conference on Artificial Intelligence (PanAfriCon AI 2024), Communications in Computer and Information Science, Vol. 2550, Springer, Cham, 2025, pp. 179–193.
A. Iqbal, F. Anjum, S. Farhan, A. Khalid, F. Kalsoom, R. Zahoor, An intelligent system for Pakistani currency recognition and fake currency detection for visually impaired, Spectrum of Engineering Sciences 3(10) (2025) 998–1015.
M. Lévano, M. Tori, B. Peralta, Deep learning-based vision system for banknote recognition: A neurocomputational approach, Proceedings of the 11th International Conference on Systems and Informatics (ICSAI), Shanghai, China, 2025, pp. 1–6.
M. Z. Haider, T. Noreen, M. Salman, Towards quantum-ready blockchain fraud detection via ensemble graph neural networks, Proceedings of the 7th International Conference on Blockchain Computing and Applications (BCCA), Dubrovnik, Croatia, 2025, pp. 898–905.
D. R. Kiran, U. Sesadri, B. P. Paruchuri, K. Karpagavalli, S. Adinarayana, S. HrushikesavaRaju, Efficient deepfake detection using a hybrid deep learning model, Proceedings of the 7th International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kalyani, India, 2025, pp. 424–429.
O. Varalakshmi, Counterfeit currency detection system using image processing and deep learning, International Journal on Science and Technology 16 (3) (2025) 1–10.
A. Almojarkesh, S. Ahmed, Qatari Money Reader: A smart mobile app to support persons with visual impairment and the elderly – Supported by Mada Innovation Program, Nafath 6 (19) (2021) 5–9.
V. Agustin, K. R. Acaba, R. L. Brieva, S. M. Dacumos, V. D. Cruz, R. Fernandez, M. R. Garcia, N. Montano, A. J. Raagas, Money identifier: An android-based application for visually impaired people using object detection, International Journal of Advanced Trends in Computer Science and Engineering 11 (3) (2022) 108–116.
S. Angsupanich, S. Matayong, A prototype real-time mobile application for Thai banknote recognition to support visually impaired persons, Universal Access in the Information Society 23 (4) (2024) 1987–2002.
State of California, California Consumer Privacy Act (2018). Accessed 22 June 2026.
Republic of Türkiye, Turkish Penal Code Law No. 5237 (2004). Accessed 22 June 2026.
Organization for Economic Co-operation and Development, OECD Artificial Intelligence Principles (2019). Accessed 22 June 2026.
P. Čerka, J. Grigienė, G. Sirbikytė, Liability for damages caused by artificial intelligence, Computer Law & Security Review 31 (3) (2015) 376–389.
P. Chhabra, S. Goyal, A two-phase deep learning model for counterfeit detection of Indian banknotes using YOLO-NAS and UV imaging for visually impaired people, Evergreen 12 (3) (2025) 1633–1653.
V. Meshram, V. Meshram, K. Patil, Y. Suryawanshi, P. Chumchu, A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification, Data in Brief 51 (2023) 109699.
S. Kumar, K. Selvakumar, S. Khan, N. Dhakad, V. Gupta, A. Sharma, A visual and comprehensive dataset of Indian currencies: Denominations ranging from one to five hundred rupees, Proceedings of the 1st International Conference on Advancement in Futuristic Technologies (ICAFT), Belagavi, India, 2025, pp. 1–8.
S. Prateeksha, C. Sandhya, V. Padmapriya, S. Uma, Sahayaka: A fake currency detector application for visually impaired individuals, Proceedings of the 2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE), Bengaluru, India, 2024, pp. 9–14.

Details

Primary Language

English

Subjects

Fairness, Accountability, Transparency, Trust and Ethics of Computer Systems

Journal Section

Research Article

Authors

Sezen Bal ^*
0000-0002-7244-6613
Türkiye

Emre Canayaz
0000-0002-3695-3642
Türkiye

Zeynep Beyza Kabataş Soyer
0009-0001-9593-3780
Türkiye

Publication Date

September 30, 2026

Submission Date

May 1, 2026

Acceptance Date

August 1, 2026

Published in Issue

Year 2026 Volume: 12 Number: 3

DOI

https://doi.org/10.28979/jarnas.1942318

IZ

https://izlik.org/JA46UM54DC

Cite

RIS / Bibtex

APA

Bal, S., Canayaz, E., & Kabataş Soyer, Z. B. (2026). Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches. Journal of Advanced Research in Natural and Applied Sciences, 12(3), 233-248. https://doi.org/10.28979/jarnas.1942318

AMA

1.Bal S, Canayaz E, Kabataş Soyer ZB. Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches. JARNAS. 2026;12(3):233-248. doi:10.28979/jarnas.1942318

Chicago

Bal, Sezen, Emre Canayaz, and Zeynep Beyza Kabataş Soyer. 2026. “Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches”. Journal of Advanced Research in Natural and Applied Sciences 12 (3): 233-48. https://doi.org/10.28979/jarnas.1942318.

EndNote

Bal S, Canayaz E, Kabataş Soyer ZB (September 1, 2026) Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches. Journal of Advanced Research in Natural and Applied Sciences 12 3 233–248.

IEEE

[1]S. Bal, E. Canayaz, and Z. B. Kabataş Soyer, “Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches”, JARNAS, vol. 12, no. 3, pp. 233–248, Sept. 2026, doi: 10.28979/jarnas.1942318.

ISNAD

Bal, Sezen - Canayaz, Emre - Kabataş Soyer, Zeynep Beyza. “Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches”. Journal of Advanced Research in Natural and Applied Sciences 12/3 (September 1, 2026): 233-248. https://doi.org/10.28979/jarnas.1942318.

JAMA

1.Bal S, Canayaz E, Kabataş Soyer ZB. Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches. JARNAS. 2026;12:233–248.

MLA

Bal, Sezen, et al. “Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches”. Journal of Advanced Research in Natural and Applied Sciences, vol. 12, no. 3, Sept. 2026, pp. 233-48, doi:10.28979/jarnas.1942318.

Vancouver

1.Sezen Bal, Emre Canayaz, Zeynep Beyza Kabataş Soyer. Detection of Accessible Counterfeit Banknotes Using Artificial Intelligence: A Comparative Analysis of Legal and Ethical Approaches. JARNAS. 2026 Sep. 1;12(3):233-48. doi:10.28979/jarnas.1942318

DOAJ	EBSCO
Scilit	SOBİAD