Vol. 1 No. 2 (2025)

Articles

A Multi-Layered Framework for Robust Real-Time Fraud Detection Against Deepfake Attacks in Digital Banking

Published 2025-11-17

  • Michele Trifiletti

Michele Trifiletti
Department of Social Sciences, Universidad Católica San Antonio de Murcia (UCAM), 30107 Murcia, Spain

DOI: https://doi.org/10.64797/rwas.v1i2.107

Abstract

The rapid evolution of generative artificial intelligence has significantly transformed the security risks faced by digital banking systems, particularly through the emergence of highly realistic synthetic audio and video capable of enabling identity fraud. These deepfake-driven threats challenge traditional biometric authentication and liveness detection mechanisms, creating critical vulnerabilities in real-time financial environments where detection systems must operate under strict latency and regulatory constraints. This paper presents a multi-layered, system-oriented framework for real-time fraud detection that reconceptualizes deepfake detection as a robustness problem rather than a standalone classification task. The proposed architecture combines physiological signal analysis using remote photoplethysmography, continuous behavioral biometrics for session-level trust evaluation, and adversarially trained neural models designed to detect artifacts associated with generative systems. The framework explicitly incorporates regulatory considerations relevant to high-risk artificial intelligence applications, including explainability, data minimization, and edge-based processing. By integrating technical resilience with practical deployment and governance requirements, this work offers a deployable model for strengthening digital banking security against emerging synthetic media attacks. The proposed framework is evaluated through a robustness-oriented analytical approach, focusing on system-level resilience rather than isolated detection accuracy. Evaluation dimensions include attack resistance, detection latency, and cross-layer consistency under adversarial conditions. The results indicate that combining heterogeneous signals reduces single-point failure risks and increases attacker complexity. Although the study does not report benchmark-based empirical validation, the proposed evaluation logic provides a structured basis for assessing deployability in adversarial, latency-constrained, and regulation-intensive financial settings. These findings support the feasibility of robustness-driven, system-level fraud detection strategies for digital banking applications.

Keywords

Deepfake Detection, Digital Banking Security, Real-Time Fraud Detection, Remote Photoplethysmography, Behavioral Biometrics, Adversarial Machine Learning, Identity Fraud Prevention, Trustworthy Artificial Intelligence

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References

  1. [1]Qureshi, S.M., Saeed, A., Almotiri, S.H., et al., 2024. Deepfake forensics: A survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ Computer Science. 10, e2037. DOI: https://doi.org/10.7717/peerj-cs.2037
  2. [2]Kaur, A., Hoshyar, A.N., Saikrishna, V., et al., 2024. Deepfake video detection: Challenges and opportunities. Artificial Intelligence Review. 57, 159. DOI: https://doi.org/10.1007/s10462-024-10810-6
  3. [3]Khan, A.A., Laghari, A.A., Inam, S.A., et al., 2025. A survey on multimedia-enabled deepfake detection: State-of-the-art tools and techniques, emerging trends, current challenges & limitations, and future directions. Discover Computing. 28, 48. DOI: https://doi.org/10.1007/s10791-025-09550-0
  4. [4]Arner, D.W., Barberis, J., Buckley, R.P., 2017. FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business. 37(3), 371–413.
  5. [5]Basel Committee on Banking Supervision, 2024. Principles for the Sound Management of of Third-Party Risk. Bank for International Settlements (BIS): Basel, Switzerland. Available from: https://www.bis.org/bcbs/publ/d577.pdf
  6. [6]Korshunov, P., Marcel, S., 2018. DeepFakes: A New Threat to Face Recognition? Assessment and Detection. arXiv preprint. arXiv:1812.08685. DOI: https://doi.org/10.48550/arXiv.1812.08685
  7. [7]Verdoliva, L., 2020. Media forensics and deepfakes: An overview. IEEE Journal of Selected Topics in Signal Processing. 14(5), 910–932. DOI: https://doi.org/10.1109/JSTSP.2020.3002101
  8. [8]Tolosana, R., Vera-Rodriguez, R., Fierrez, J., et al., 2020. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion. 64, 131–148. DOI: https://doi.org/10.1016/j.inffus.2020.06.014
  9. [9]Matern, F., Riess, C., Stamminger, M., 2019. Exploiting visual artifacts to expose deepfakes and face manipulations. In Proceedings of the IEEE Winter Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA, 7–11 January 2019; pp. 83–92. DOI: https://doi.org/10.1109/WACVW.2019.00020
  10. [10]Szegedy, C., Zaremba, W., Sutskever, I., et al., 2014. Intriguing properties of neural networks. International Conference on Learning Representations (ICLR). arXiv preprint. arXiv:1312.6199. DOI: https://arxiv.org/abs/1312.6199
  11. [11]Goodfellow, I., Shlens, J., Szegedy, C., 2015. Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR). arXiv preprint. arXiv:1412.6572. DOI: https://arxiv.org/abs/1412.6572
  12. [12]Biggio, B., Roli, F., 2018. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition. 84, 317–331. DOI: https://doi.org/10.1016/j.patcog.2018.07.023
  13. [13]Galbally, J., Marcel, S., Fierrez, J., 2014. Biometric anti-spoofing methods: A survey in Face. IEEE Access. 2, 1530–1552. DOI: https://doi.org/10.1109/ACCESS.2014.2381273
  14. [14]ISO/IEC 30107-3:2023. 2023. Information Technology—Biometric Presentation Attack Detection—Part 3: Testing and Reporting.
  15. [15]The European Parliament, the Council of the European Union, 2018. Directive (EU) 2018/843 of the European Parliament and of the Council of 30 May 2018 Amending Directive (EU) 2015/849 on the Prevention of the Use of the Financial System for the Purposes of Money Laundering or Terrorist Financing, and Amending Directives 2009/138/EC and 2013/36/EU (Text with EEA Relevance). Official Journal of the European Union: Luxembourg, Luxembourg. Available from: http://data.europa.eu/eli/dir/2018/843/oj
  16. [16]European Banking Authority, 2022. Final Report: Guidelines on the Use of Remote Customer Onboarding Solutions under Article 13(1) of Directive (EU) 2015/849. EBA: Paris, France.
  17. [17]The European Parliament, the Council of the European Union, 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA Relevance). Official Journal of the European Union: Luxembourg, Luxembourg. Available from: http://data.europa.eu/eli/reg/2016/679/oj
  18. [18]The European Parliament, the Council of the European Union, 2024. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 Laying Down Harmonised Rules on Artificial Intelligence and Amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA Relevance). Official Journal of the European Union: Luxembourg, Luxembourg. Available from: http://data.europa.eu/eli/reg/2024/1689/oj
  19. [19]National Institute of Standards and Technology, 2023. The EU Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. NIST: Gaithersburg, MD, USA.
  20. [20]Carlini, N., Wagner, D., 2017. Towards evaluating the robustness of neural networks. In Proceedings of the IEEE Symposium on Security and Privacy, San Jose, CA, USA, 22–26 May 2017; pp. 39–57.
  21. [21]Salvi, D., Liu, H., Mandelli, S., et al., 2023. A Robust Approach to Multimodal Deepfake Detection. Journal of Imaging. 9(6), 122. DOI: https://doi.org/10.3390/jimaging9060122
  22. [22]Ramanaharan, R., Guruge, D.B., Agbinya, J.I., 2025. DeepFake video detection: Insights into model generalisation—A systematic review. Data and Information Management. 9(3), 100099. DOI: https://doi.org/10.1016/j.dim.2025.100099
  23. [23]de Haan, G., Jeanne, V., 2013. Robust pulse rate from chrominance-based rPPG. IEEE Transactions on Biomedical Engineering. 60(10), 2878–2886.
  24. [24]Nowara, E.M., Sabharwal, A., Veeraraghavan, A., 2017. PPGSecure: Biometric presentation attack detection using photoplethysmograms. In Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 30 May–3 June 2017.
  25. [25]Hernandez-Ortega, J., Tolosana, R., Fierrez, J., et al., 2020. DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation. arXiv preprint. arXiv:2010.00400. DOI: https://doi.org/10.48550/arXiv.2010.00400
  26. [26]Drozdowski, P., Rathgeb, C., Dantcheva, A., et al., 2020. Demographic Bias in Biometrics: A Survey on an Emerging Challenge. IEEE Transactions on Technology and Society. 1(2), 89–103.
  27. [27]Upadhyaya, S.J., 2017. Continuous authentication using behavioral biometrics. In IWSPA '17: Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics. Association for Computing Machinery: New York, NY, USA. DOI: https://doi.org/10.1145/3041008.3041019
  28. [28]Acien, A., Morales, A., Vera-Rodriguez, R., et al., 2020. Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns. In: Bourlai, T., Karampelas, P., Patel, V.M. (Eds.). Securing Social Identity in Mobile Platforms. Advanced Sciences and Technologies for Security Applications. Springer: Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-030-39489-9_9
  29. [29]Li, Y., Chang, M.-C., Lyu, S., 2018. In Ictu Oculi: Exposing AI-generated fake face videos by detecting eye blinking. arXiv preprint. arXiv:1806.02877. DOI: https://doi.org/10.48550/arXiv.1806.02877
  30. [30]Agarwal, S., Farid, H., Gu, Y., et al., 2019. Protecting world leaders against deepfakes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 16–20 June 2019.
  31. [31]Dal Pozzolo, A., Bontempi, G., Snoeck, M., et al., 2018. Adversarial drift detection in financial fraud. In DEC '23: Proceedings of the Second ACM Data Economy Workshop. Association for Computing Machinery: New York, NY, USA. DOI: https://doi.org/10.1145/3600046.3600051
  32. [32]Raghavan, M., Barocas, S., Kleinberg, J., et al., 2020. Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), Barcelona, Spain, 27–30 January 2020; pp. 469–481. DOI: https://doi.org/10.1145/3351095.3372828