AI Powered Fake Video and Misinformative Detection

The rapid growth of social media has accelerated the creation and spread of fake videos, deepfakes, and misleading content, posing serious risks to public trust, security, and digital integrity. This research presents an AI-powered system for automated fake video and misinformation detection, combining computer vision, deep learning, and natural language processing to analyze both visual and

contextual cues. The proposed framework integrates convolutional neural networks (CNNs) and transformer-based architectures to detect frame-level manipulation patterns, facial inconsistencies, unnatural motion artifacts, and audio-visual mismatches. Additionally, metadata analysis and cross-referencing with verified information sources help identify misinformation embedded within

video narratives. Experimental results demonstrate high accuracy in detecting deepfake artifacts and misleading claims across diverse datasets. The system provides a scalable, real-time solution suitable for social media platforms, digital forensics, and content verification agencies. This work contributes to enhancing online safety by offering a reliable AI-driven approach to counter the growing threat of fabricated and misinformative video content.