About This Project - The Deepfake Detection System is an AI-driven platform designed to identify manipulated digital media, including videos, images, and audio. It integrates multiple deep learning models to analyze visual artifacts, motion inconsistencies, and synthetic voice patterns. The system is deployed through a Flask-based web application that provides users with authenticity scores, visual explanations, and secure access features. Key Points : - Detects fake videos, images, and audio using advanced deep learning - Combines temporal and frame-level analysis for video deepfake detection - Uses ResNeXt50 + LSTM for motion inconsistency analysis in videos - Applies Xception Network for frame-level visual artifact detection - Uses EfficientNet-B0 for image authenticity classification - Integrates audio deepfake detection using spectrogram-based CNN models to analyze synthetic voice patterns - Built with a Flask backend and HTML/CSS/JavaScript frontend - Powered by PyTorch and TensorFlow/Keras frameworks - Provides fake probability scores, confidence graphs, and heatmaps for explainability - Includes a secure user authentication system Accuracy : ~96% overall detection accuracy across video, image, and audio deepfake datasets.