Guardian AI: Synthetic Media Forensics through Multimodal Fusion and Advanced Machine Learning

The burgeoning spread of synthetic media disrupts content verification and threatens online trust. This research proposes Guardian AI, a robust deepfake detection system achieving 93% accuracy by harnessing the synergistic power of facial recognition, image forensics, and machine learning. Guardian AI extracts diverse features from videos: facial recognition models analyze landmarks, expressions, and lip-syncing for inconsistencies; image forensics algorithms detect manipulated pixels, lighting patterns, and compression artifacts; and temporal analysis captures unnatural head movements and frame-to-frame motion discrepancies. These multifaceted features are then fused and fed into a rigorously trained deep learning model on multi-modal datasets of real and deepfake videos. Guardian AI classifies video inputs as real or fake, providing a confidence score for its prediction. By leveraging facial recognition's subtle inconsistency detection, image forensics' manipulation artifact identification, and machine learning's robust multi-cue integration, Guardian AI achieves exceptional accuracy and generalizability, adapting to evolving deepfake creation techniques with its diverse training data. This study signifies a significant contribution to content verification by delivering a high accuracy deepfake detection system, paving the way for a more reliable and trustworthy online environment.