FakeDetect is a cutting-edge, multimodal deepfake and fake news detection system designed for enterprise security and media verification. It uses state-of-the-art AI models to analyze Audio, Images, and Videos for signs of manipulation, synthesis, and deepfakes. Features: - Audio Detection: Analyzes wave patterns and voice anomalies to identify synthetic or cloned audio using a Wav2Vec2 sequence classifier. - Image Analysis: Detects AI-generated images, face swaps, and pixel-level manipulation using an EfficientNet V2 model. - Video Verification: Analyzes videos frame-by-frame and temporally using an Xception-based architecture. * Dynamic Heatmaps: Automatically generates temporal probability heatmaps showing the fake probability segment-by-segment. * Frame Extraction: Displays exactly which frames were analyzed by the engine for complete transparency. Tech Stack: - Backend: Python, FastAPI, TensorFlow/Keras (tf-keras), PyTorch, OpenCV, Librosa - Frontend: HTML5, Vanilla JS, CSS (Responsive, Modern UI) - Data Visualization: Matplotlib, Seaborn (for dynamic heatmaps) Overall Accuracy : 92%