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

Cognitive image authentication leverages unique behavioural and cognitive traits for secure user authentication. Traditional text-based passwords, while prevalent, are vulnerable to security breaches due to user tendency to choose simple, easily guessed passwords. Biometric methods, particularly face recognition, have gained popularity for their enhanced security, but face challenges such as susceptibility to spoofing and limitations with age-related variations or face accessories. This study introduces a cloud-based cognitive image authentication framework that combines facial recognition with cognitive biometric features to improve security and reliability. Key contributions include optimizing facial image transmission from handheld devices to the cloud, enhancing authentication accuracy despite aging or accessories, and comparing cognitive authentication with existing methods. Experimental results demonstrate that the proposed MobileNetV2-based model achieves high accuracy and robustness in real-time and large-scale datasets, making it a suitable solution for cloud-based authentication.