AI-Based Facial-Age Detection and IoT for Enhanced Data Security in Social Media

Abstract

Introduction: The growth of social media and the increased teenage interactions have raised concerns about data security and user authentication. IoT and RFID advancements, coupled with AI, have tripled data collection and transmission, increasing risks of privacy breaches and unauthorized access. Aim: This study aims to develop an AI-driven Facial-Age Detection and social media content segmentation system integrated with IoT, RFID, and GPS to enhance social media security and prevent unauthorized access by underage users and fraudsters. Problem: Data security issues arise from uncontrolled network traffic, leading to storage control, remote access challenges, and user authentication failures. Unauthorized users exploit internet data for personal gain without detection. Significance: To ensure a secure digital environment that eliminates ID duplication, reduces energy consumption, and mitigates data roaming issues. Method: A model using AI-driven Facial-Age Detection and deep learning filters was developed to filter underage users and detect fake profiles. Results: Findings confirmed the model’s effectiveness in improving user authentication and data security. Conclusion: The model exhibits a strong ”security for information” with more secure, transparent, and efficient approach in filtering underage users and fake profiles than traditional methods.