VERIFACE Fake Media and Content Detection System

Social media platforms have rapidly evolved into one of the most influential mediums for global communication, content sharing, and digital interaction. However, this widespread adoption has also introduced significant challenges, including the proliferation of deepfake videos, toxic comments, misinformation, and inappropriate or explicit images. These issues pose serious threats to user safety, platform integrity, and public trust, making effective content moderation an essential requirement for modern digital ecosystems. To address these challenges, this paper presents VERIFACE, an AI-powered social media content verification and moderation system designed to ensure a secure, authentic, and reliable online environment. The proposed system integrates advanced deep learning and Natural Language Processing (NLP) techniques to automatically analyze and moderate multimedia content in real time. For deepfake detection, VERIFACE employs a hybrid model that combines Convolutional Vision Transformers (CvT) with Long Short-Term Memory (LSTM) networks, enabling the system to effectively capture both spatial features and temporal inconsistencies in video data. Image- based content is analyzed using transformer-based classification models to detect vulgar or inappropriate visuals with high accuracy. The system architecture is designed for scalability and real-time performance, ensuring seamless integration with existing social media platforms. By automating the detection and filtering of harmful content, VERIFACE significantly reduces reliance on manual moderation and enhances operational efficiency. Overall, the proposed system improves content authenticity, minimizes harmful interactions, and promotes a safer, more transparent, and trustworthy digital communication environment for users worldwide.