E-passport security systems and attack implications
Abstract
Recent technological advances aim to recognise user data using biometrics such as the face, fingerprint, hand veins and iris. Currently, face prints are widely used to verify user data in e-passports. As a result, institutions face substantial difficulties in maintaining an appropriate level of security. Human error can introduce flaws that undermine security mechanisms. One potential solution to this problem is to install a facial recognition security system. Both hardware and software components make up this system, with the hardware being a camera and the software comprising face detection and identification algorithms.
The purpose of this essay is to provide a thorough understanding of the Face Recognition Security System, including its application and deployment. Furthermore, the essay investigates the various weaknesses and methods of attack that could be used to target the system. The purpose of addressing these factors is to improve the effectiveness and robustness of system security, notably e-passport security.
References
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Sharma, G., Patidar, G., Vishwakarma, H., & Shringi, D. (2021) Future of cybersecurity: a study on biometric scans.
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Skeba, P., & Baumer, E. P. (2020) Informational friction as a lens for studying algorithmic aspects of privacy. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), pp. 1-22.
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Yang, X. W. L. (2019) Face anti-spoofing: model matters, so does data. USA: IEEE.
Yang, J. Z. L. (2014) Learn convolutional neural network for face anti-spoofing. Computer Science.
Alshammari, K., Beach, T., Rezgui, Y., & Alelwani, R. (2023) Built environment cybersecurity: development and validation of a semantically defined access management framework on a university case study. Applied Sciences, 13(13), p.7518.
Atoum, Y. Y. L. (2017) Face anti-spoofing using patch and depth-based cnns. USA: Proceedings of the IEEE International Joint Conference on Biometrics, Denver.
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) Recent advances in adversarial training for adversarial robustness. arXiv preprint arXiv:2102.01356.
bin Suhaimi, A. I. H., Noordin, N., & bin Ya’kub, M. F. (2020) Assessment of Malaysian e-passport PKI based on ISO 27000 series international standards. In Journal of Physics: Conference Series, 1551(1), p. 012003. IOP Publishing.
Bodepudi, A., & Reddy, M. (2020) Spoofing Attacks and Mitigation Strategies in Biometrics-as-a-Service Systems. Eigenpub Review of Science and Technology, 4(1), pp. 1-14.
Boutros, F., Struc, V., Fierrez, J., & Damer, N. (2023) Synthetic data for face recognition: Current state and future prospects. Image and Vision Computing, 104688.
Butpheng, C., Yeh, K. H., & Xiong, H. (2020) Security and privacy in IoT-cloud-based e-health systems—A comprehensive review. Symmetry, 12(7), pp. 1191.
Choudhury, Z. H. (2022) Encryption and encoding of facial images into quick response and high capacity color 2d code for biometric passport security system. arXiv preprint arXiv:2203.15738.
Das, S., Wang, B., Tingle, Z., & Camp, L. J. (2019) Evaluating user perception of multi-factor authentication: A systematic review. arXiv preprint arXiv:1908.05901.
Devabhakthini, P., Parida, S., Shukla, R. M., & Nayak, S. C. (2023) Analyzing the Impact of Adversarial Examples on Explainable Machine Learning. arXiv preprint arXiv:2307.08327.
Fang, M., Damer, N., Kirchbuchner, F., & Kuijper, A. (2022) Real masks and spoof faces: On the masked face presentation attack detection. Pattern Recognition, 123, 108398.
Fang, M., Boutros, F., & Damer, N. (2023) Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains. In Handbook of Biometric Anti-Spoofing: Presentation Attack Detection and Vulnerability Assessment (pp. 171-199) Singapore: Springer Nature Singapore.
Ferrara, M., & Franco, A. (2022) Morph Creation and Vulnerability of Face Recognition Systems to Morphing. In Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks (pp. 117-137) Cham: Springer International Publishing.
Fourati, E., Elloumi, W., & Chetouani, A. (2020) Anti-spoofing in face recognition-based biometric authentication using image quality assessment. Multimedia Tools and Applications, 79(1-2), pp. 865-889.
Fubara-Manuel, I. (2020) Biometric capture: disrupting the digital codification of black migrants in the UK. African Diaspora, 12(1-2), pp. 117-141.
Gupta, B. B., & Quamara, M. (2019) Smart Card Security: Applications, Attacks, and Countermeasures. CRC Press.
Hajare, H. R., & Ambhaikar, A. (2023) Face Anti-Spoofing Techniques and Challenges: A short survey. In 2023 11th International Conference on Emerging Trends in Engineering & Technology-Signal and Information Processing (ICETET-SIP), pp. 1-6. IEEE.
Hanley, M., Barocas, S., Levy, K., Azenkot, S., & Nissenbaum, H. (2021) Computer vision and conflicting values: Describing people with automated alt text. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 543-554.
Hasnine, M. N., Bui, H. T., Tran, T. T. T., Nguyen, H. T., Akçapınar, G., & Ueda, H. (2021) Students’ emotion extraction and visualization for engagement detection in online learning. Procedia Computer Science, 192, pp.3423-3431.
JameerModi, S., Shaikh, T. R., & Mane, S. V. (2022) Automatic Door Opening with Face Recognition and Temperature Detecting Device. NeuroQuantology, 20(12), p. 3272.
Jia, Y. J. Z. (2020) Single-side domain generalization for face anti-spoofing. USA: Proceedings of the 2020 IEEE.
Kollreider, K. H. F. (2017) Real-time face detection and motion analysis with application in “liveness. USA: IEEE Transactions on Information Forensics and Security.
Lei, L. X. F. (2017) An original face anti-spoofing approach using partial convolutional neural network. USA: in Proceedings of the International Conference on Image Processing Theory Tools & Applications.
Li, H. P. H. (2018) Learning generalized deep feature representation for face anti-spoofing. IEEE Transactions on Information Forensics and Security.
Liu, Y. A. J. (2019) Learning deep models for face anti-spoofing: binary or auxiliary supervision. USA: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.
Mandal, J. K., & Bhattacharya, D. (2020) Emerging technology in modelling and graphics. Springer Singapore.
Marcel, J. G. (2017) Face anti-spoofing based on general image quality assessment. Sweden: Proceedings of the International Conference on Pattern Recognition IEEE Computer Society.
Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied Intelligence, 53(4), pp. 3974-4026.
Micheletto, M. (2023) Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective.
Ming, Z., Visani, M., Luqman, M. M., & Burie, J. C. (2020) A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices. Journal of Imaging, 6(12), p. 139.
Moolla, Y., De Kock, A., Mabuza-Hocquet, G., Ntshangase, C. S., Nelufule, N., & Khanyile, P. (2021) Biometric recognition of infants using fingerprint, iris, and ear biometrics. IEEE Access, 9, pp. 38269-38286.
More, C. S., Kumar, S. R. N., Chatterji, S., & Prawal, P. (2022) Detection of Face and Location of Driver for Vehicle Theft using RaspberryPi.
Natsheh, E., & Said-Ahmed, H. (2022) Authentication System by Facial Recognition with Principal Component Analysis and Deep Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 10(4), pp. 179-183.
Roussi, A. (2020) Resisting the rise of facial recognition. Nature, 587(7834), pp. 350-354.
Rowe, E. A. (2020) Regulating facial recognition technology in the private sector. Stan. Tech. L. Rev., 24, 1.
Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R., & Busch, C. (2019) Face recognition systems under morphing attacks: A survey. IEEE Access, 7, pp. 23012-23026.
Sharma, D., & Selwal, A. (2023) A survey on face presentation attack detection mechanisms: hitherto and future perspectives. Multimedia Systems, 29(3), pp. 1527-1577.
Sharma, G., Patidar, G., Vishwakarma, H., & Shringi, D. (2021) Future of cybersecurity: a study on biometric scans.
Sharma, S., & Guleria, K. (2022) Deep learning models for image classification: comparison and applications. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1733-1738, IEEE.
Skeba, P., & Baumer, E. P. (2020) Informational friction as a lens for studying algorithmic aspects of privacy. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), pp. 1-22.
Srinivas, T. S., Goutham, T., & Kumaran, M. S. (2022) Face Recognition based Smart Attendance System Using IoT.
Syed, F., Gupta, S. K., Hamood Alsamhi, S., Rashid, M., & Liu, X. (2021) A survey on recent optimal techniques for securing unmanned aerial vehicles applications. Transactions on Emerging Telecommunications Technologies, 32(7), e4133.
Telo, J. (2023) Smart City Security Threats and Countermeasures in the Context of Emerging Technologies. International Journal of Intelligent Automation and Computing, 6(1), pp. 31-45.
Vignesh, T., Thyagharajan, K. K., & Jeyavathana, R. B. (2022) An improved E-passport system with secured IoT and wireless communication technology. In AIP Conference Proceedings, 2452(1) AIP Publishing
Yang, X. W. L. (2019) Face anti-spoofing: model matters, so does data. USA: IEEE.
Yang, J. Z. L. (2014) Learn convolutional neural network for face anti-spoofing. Computer Science.
Authors
Alshammari, K. (2023). E-passport security systems and attack implications . International Journal of Advanced Science and Computer Applications, 3(1), 75–80. https://doi.org/10.47679/ijasca.v3i1.38
This work is licensed under a Creative Commons Attribution 4.0 International License.