Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, cyber security. To assure secure transactions to protect user's identities against cybersecurity threats, strong authentication techniques should be used. The prevalence biometrics, such as fingerprints, in identification makes the need safeguard them across different areas applications. Our study presents a system detect alterations biometric modalities discriminate pristine, adulterated, fake biometrics 5G-based cities. Specifically, we use deep learning models based on convolutional neural (CNN) hybrid model that combines CNN with long-short term memory (ConvLSTM) compute three-tier probability has been tempered. Simulation-based experiments indicate alteration detection accuracy matches those recorded advanced methods superior performance terms detecting central rotation fingerprints. This proposed veritable solution