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Cloud-Based Cognitive Image Authentication Using Deep Learning Techniques for Secure Access

Year: 2024
Type: article
Abstract: Cognitive image authentication systems implement specialized behavioral and cognitive parameters foraccurate and secure user verification. This paper presents a dual-Face cognitive Cloud image authentication frameworkfor improved accuracy and secured cognitive biometric features. The current work addresses the endpoint weaknessesof malicious hacking and biometric systems. The MobileNetV2 model along with XOR segmentation and encryptionsafeguard the system‘s real-time authentication. Essential features include optimizing facetime image and video overthe handheld swiping devices to the cloud and the retention of authentication accuracy regardless of ageing, accessories,and vanity. Additionally, the performance of MobileNetV2 cognitive authentication and the rest of the systems weretested and analyzed. An experimental result comparison with the rest of the systems showed that the proposedMobileNetV2 based model achieved over 95.3% accuracy and a 91.11% F1 score for real-time and large-scale datasetsrespectively, outperforming the rest of the models including CNN, ResNet50, VGG19, hence able to work as asatisfactory cloud-based authentication. The proposed system showed response time for the XOR encryption method of55ms to 75ms, which was also 63-68% faster than the proposed system using AES encryption. Cross-architectureperformance evaluation with several deep learning systems confirmed the performance of MobileNetV2 as the bestavailable for cloud scalable authentication. The proposed system offers a major cognitive biometrics advancement byproviding a lightweight, efficient and robust authentication framework.
DOI: 10.52710/cfs.52
Authors: Pranali Dahiwal, Vijay Khare
Language: English
Open Access status: diamond
OpenAlex ID: W4406966209
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