International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 4, 8 Aug. 2026
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Cloud-Based Cognitive Image Authentication Using Deep Learning Techniques for Secure Access
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Author(s)
Pranali Dahiwal
1,*
Vijay Khare
2
Index Terms
Cognitive Authentication, Biometric Authentication, Face Recognition, Cloud Computing, Mobilenetv2, Image Transmission, Security
Abstract
Cognitive image authentication systems implement specialized behavioral and cognitive parameters for accurate and secure user verification. This paper presents a dual-face cognitive cloud image authentication framework for improved accuracy and secured cognitive biometric features. The current work addresses the endpoint weaknesses of malicious hacking and biometric systems. The MobileNetV2 model, along with XOR segmentation and encryption, safeguards the system’s real-time authentication. Essential features include optimizing facetime image and video over the handheld swiping devices to the cloud and the retention of authentication accuracy regardless of ageing, accessories, and appearance variations. Additionally, the performance of MobileNetV2 cognitive authentication and the rest of the systems were tested and analyzed. An experimental result comparison with the rest of the systems showed that the proposed MobileNetV2 based model achieved 95.3% accuracy and a 91.11% F1 score for real-time and large-scale datasets respectively, outperforming the rest of the models including CNN, ResNet50, VGG19, hence it can serve as a satisfactory cloud-based authentication framework. The proposed system showed response time for the XOR encryption method of 55ms to 75ms, which was also 63-68% faster than the proposed system using AES encryption. Cross-architecture performance evaluation with several deep learning systems confirmed the performance of MobileNetV2 as the best available for cloud scalable authentication. The proposed system offers a major cognitive biometrics advancement by providing a lightweight, efficient and robust authentication framework.
Cite This Paper
Pranali Dahiwal, Vijay Khare, "Cloud-Based Cognitive Image Authentication Using Deep Learning Techniques for Secure Access", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.4, pp. 233-250, 2026. DOI:10.5815/ijcnis.2026.04.12
Reference
[1]P. Su, "Immersive online biometric authentication algorithm for online guiding based on face recognition and cloud-based mobile edge computing," Distributed and Parallel Databases, vol. 41, pp. 133–154, 2023.
[2]M. S. Hossain, G. Muhammad and M. Al Qurishi, "Verifying Image Authenticity in Cognitive Internet of Things (CIoT)-Oriented Cyber Physical System," Mobile Networks and Applications, vol. 23, pp. 239-250, 2018.
[3]A. H. Sodhro, C. Sennersten and A. Ahmad, "Towards cognitive authentication for smart healthcare applications," Sensors, vol. 22, p. 2101, 2022.
[4]N. I. Mowla, I. Doh and K. Chae, "On-device AI-based cognitive detection of bio-modality spoofing in medical cyber physical system," IEEE Access, vol. 7, pp. 2126-2137, 2019.
[5]Z. Yu, J. Komulainen, X. Li, and G. Zhao, “Review of Face Presentation Attack Detection Competitions,” in Handbook of Biometric Anti-Spoofing, 3rd ed., Advances in Computer Vision and Pattern Recognition, Springer, Singapore, pp. 287–336, 2023.
[6]S. Bhattacharjee, A. Mohammadi, A. Anjos, and S. Marcel, “Recent Advances in Face Presentation Attack Detection,” in Handbook of Biometric Anti-Spoofing, 2nd ed., Advances in Computer Vision and Pattern Recognition, Springer, Cham, pp. 207–228, 2019.
[7]A. Kaklauskas, A. Abraham, I. Ubarte, R. Kliukas, V. Luksaite, A. Binkyte-Veliene, I. Vetloviene and L. Kaklauskiene, "A review of AI cloud and edge sensors, methods, and applications for the recognition of emotional, affective and physiological states," Sensors, vol. 22, p. 7824, 2022.
[8]M. M. Ahsan, K. D. Gupta, A. K. Nag, S. Poudyal, A. Z. Kouzani and M. A. P. Mahmud, "Applications and evaluations of bio-inspired approaches in cloud security: A review," IEEE Access, vol. 8, pp. 180799-180814, 2020.
[9]P. Kairouz, H. B. McMahan, et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends in Machine Learning, vol. 14, nos. 1-2, pp. 1-210, 2021.
[10]A. T. Alrahlawee, A. D. Duru , O. Bayat and O. N. Uçan, "Cloud authentication based face recognition technique," AURUM Journal of Engineering Systems and Architecture, vol. 3, pp. 79-96, 2019.
[11]S. O. Olabanji, O. O. Olaniyi, C. S. Adigwe, O. J. Okunleye and T. O. Oladoyinbo, "AI for identity and access management (IAM) in the cloud: Exploring the potential of artificial intelligence to improve user authentication, authorization, and access control within cloud-based systems," Asian Journal of Research in Computer Science, vol. 17, pp. 38-56, 2024.
[12]E. A. W. Hachim, M. T. Gaata and T. Abbas, "Voice-authentication model based on deep learning for cloud environment," International Journal on Informatics Visualization, vol. 7, pp. 864-870, 2023.
[13]H. Jamil, A. Ali, M. Ammi, R. Kirichek, M. S. A. Muthanna and F. Jamil, "Machine Learning–Based Identity and Access Management for Cloud Security," in Secure Edge and Fog Computing Enabled AI for IoT and Smart Cities, EAI/Springer Innovations in Communication and Computing, Springer, Cham, 2024, pp. 195–207.
[14]N. Subramanian, S. Nikkath Bushra, G. Shobana, and S. Radhika, "An optimal modified bidirectional generative adversarial network for security authentication in cloud environment," Cybernetics and Systems, pp. 1–33, 2024.
[15]S. Poomalai, K. Venkatesan, S. Subbaraj and S. Radha, "Secure and privacy improved cloud user authentication in biometric multimodal fusion using blockchain-based lightweight deep instance-based DetectNet," Network: Computation in Neural Systems, vol. 35, pp. 300-318, 2024.
[16]C. Venkatachalam, K. Manivannan and S. Venkatachalam, "Securing data in the cloud: The application of fuzzy identity biometric encryption for enhanced privacy and authentication," Lecture Notes in Networks and Systems, vol. 798, pp. 213-224, 2023.
[17]R. Shah and S. K. Dubey, "Multi user authentication for reliable data storage in cloud computing," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, pp. 82-89, 2024.
[18]K. A. Patel, S. Shukla and S. J. Patel, "A novel and provably secure mutual authentication protocol for cloud environment using elliptic curve cryptography and fuzzy verifier," Concurrency and Computation: Practice and Experience, vol. 36, p. e7889, 2023.
[19]M. Gupta, L. Ahuja and A. Seth, "Security enhancement in a cloud environment using a hybrid chaotic algorithm with multifactor verification for user authentication," International Journal of Computer Applications, vol. 45, pp. 680–696, 2023.
[20]H. Patwal, R. Kumar, I. Ahamad, A. Mittal, H. Singh and S. Goyal, "Facial recognition in cloud security: Research perspectives on authentication solutions," 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1869-1874, 2024.
[21]X. Qi, C. Wu, H. Qi, Y. Shi, K. Duan and X. Wang, "A real-time face detection method based on blink detection," IEEE Access, vol. 11, pp. 28180–28189, 2023.
[22]J. Chen, J. Chen, Z. Wang, C. Liang and C. W. Lin, "Identity-aware face super-resolution for low-resolution face recognition," IEEE Signal Processing Letters, vol. 27, pp. 645–649, 2020.
[23]Q. Wang, T. Wu, H. Zheng and G. Guo, "Hierarchical pyramid diverse attention networks for face recognition," IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8323–8332, 2020.
[24]Q. Cao, L. Shen, W. Xie, O. M. Parkhi and A. Zisserman, "VGGFace2: A dataset for recognising faces across pose and age," 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 67-74, 2018.
[25]Y. Zheng, D. K. Pal and M. Savvides, "Ring loss: Convex feature normalization for face recognition," IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5089-5097, 2018.
[26]M. Sarkhoshi and Q. Li, "Cognitive graphical password based on recognition with improved user functionality," 12th International Conference on Computer Science and Information Technology (CCSIT), pp. 17-24, 2022.
[27]C. Katsini, C. Fidas, M. Belk, G. Samaras and N. Avouris, "A human-cognitive perspective of users’ password choices in recognition-based graphical authentication," International Journal of Human–Computer Interaction, vol. 35, pp. 1800–1812, 2019.
[28]S. Wiedenbeck, J. Waters, J. C. Birget, A. Brodskiy and N. Memon, "PassPoints: Design and longitudinal evaluation of a graphical password system," International Journal of Human-Computer Studies, vol. 63, pp. 102-127, 2005.
[29]P. Dunphy, J. Nicholson and P. Olivier, "Securing passfaces for description," Proceedings of the 4th symposium on Usable privacy and security, pp. 24-33, 2008.
[30]M. Faraki, X. Yu, Y.-H. Tsai, Y. Suh, and M. Chandraker, “Cross-Domain Similarity Learning for Face Recognition in Unseen Domains,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 15287–15296, 2021.
[31]J. Guo, X. Zhu, C. Zhao, D. Cao, Z. Lei, and S. Z. Li, “Learning Meta Face Recognition in Unseen Domains,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 6162–6171, 2020.
[32]V. Shejwalkar and A. Houmansadr, “Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning,” in Proc. Network and Distributed System Security Symp. (NDSS), 2021.