A Review on Biometric Security & Cybercrime: Evaluating Vulnerabilities in Fingerprint and Facial Recognition Systems
Keywords:
Biometric security, Biometric vulnerabilities, Cybercrime, Fingerprint spoofing, Facial recognition hackingAbstract
Biometric authentication has revolutionized security systems by offering a more reliable and user-friendly alternative to traditional password-based authentication. It is widely implemented in areas such as banking, smartphones, and border control, relying on unique physiological traits like fingerprints and facial features. However, despite its growing adoption, biometric security faces significant vulnerabilities. Research highlights that biometric systems are increasingly susceptible to spoofing techniques, including artificial fingerprint replication and deep fake-based facial forgeries, which allow attackers to bypass authentication. Moreover, advancements in AI-driven attacks, such as adversarial machine learning, have further challenged the effectiveness of biometric systems.
To address these vulnerabilities, researchers have developed security enhancements such as liveness detection, AI-powered fraud detection, and multimodal authentication, which integrate multiple biometric modalities for increased reliability. However, biometric data breaches present a significant concern, as unlike passwords, compromised biometric identifiers cannot be reset, making identity theft a long-term risk. This study explores the vulnerabilities of biometric security, real-world attack scenarios, and advanced countermeasures designed to strengthen biometric authentication. Furthermore, regulations and compliance frameworks are being developed to ensure ethical and secure biometric deployment. As cyber threats continue to evolve, it is crucial to develop adaptive and resilient security strategies to ensure the long-term integrity and trustworthiness of biometric systems.
References
A. K. Jain, A. A. Ross, and K. Nandakumar, Introduction to Biometrics, 1st ed. New York, NY, USA: Springer, 2011, pp. 141–174. https://link.springer.com/book/10.1007/978-0-387-77326-1
W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey,” ACM Comput. Surv. (CSUR), vol. 35, no. 4, pp. 399–458, Dec. 2003. https://doi.org/10.1145/954339.954342
S. Wu and Y. Zhu, “Constant-Round Password-Based Authenticated Key Exchange Protocol for Dynamic Groups,” Lecture Notes in Computer Science, pp. 69–82, doi: https://doi.org/10.1007/978-3-540-85230-8_6
P. Grother, M. Ngan, and K. Hanaoka, "Face recognition vendor test (FRVT) Part 2: Identification," NIST, Sep. 13, 2019. https://doi.org/10.6028/NIST.IR.8271
Y. Liu, H. Yu, C. Gong, and Y. Chen, "A real-time expert system for anomaly detection of aerators based on computer vision and surveillance cameras," J. Vis. Commun. Image Represent. vol. 68, p. 102767, Apr. 2020. https://doi.org/10.1016/j.jvcir.2020.102767
B. Alzahrani and F. Alsolami, "Biometric System: Security Challenges and Solutions," in Proceedings of the 16th International Conference on Information Technology - New Generations (ITNG 2019), 2019, pp. 111-117, Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-14070-0_17
R. Zabih, “The 30th Anniversary of the IEEE Transactions on Pattern Analysis and Machine Intelligence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 1–1, Jan. 2010, doi: https://doi.org/10.1109/tpami.2010.8
K. D. Pandl, S. Thiebes, M. Schmidt-Kraepelin, and A. Sunyaev, “On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda,” IEEE Access, vol. 8, pp. 57075–57095, 2020, doi: https://doi.org/10.1109/access.2020.2981447
A. K. Jain and S. Z. Li, Handbook of Face Recognition. New York: Springer, 2011. https://doi.org/10.1007/978-0-85729-932-1
J. M., Tien, and C. C. Uichanco, “IEEE Transactions on Systems, Man, and Cybernetics: Bibliographic Analysis and Policy Considerations,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 6, pp. 1245–1259, Nov. 1992. https://doi.org/10.1109/21.199454
R, Kiefer, Stevens, J., Patel, A., & Patel, M., "A survey on spoofing detection systems for fake fingerprint presentation attacks," In Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2020, Volume 1, Springer Singapore, 2021, pp. 315–334. https://link.springer.com/chapter/10.1007/978-981-15-7078-0_30