🎙️👁️ Deepfake Detection System
Project Overview
This project implements the Deepfake Detection System, a robust web-based application for real-time deepfake detection. It combines a state-of-the-art Audio-Visual Synchronization and Fusion Framework (AVSFF) (as the planned ML backend) with a sleek, professional React frontend designed for forensic analysts.
The system focuses on identifying deepfakes by detecting subtle inconsistencies between lip movements and audio tracks, offering enhanced accuracy over traditional image-artifact methods.
(Representative dashboard visualization)
Key Features
Multimodal Analysis: Designed to fuse visual (lip dynamics) and auditory (speech) features for robust detection.
Real-Time Processing: Optimized UI/UX for rapid video scanning and analysis.
Forensic Dashboard: A comprehensive interface displaying video playback, artifact heatmaps, frequency domain analysis, and detailed verdict confidence.
User Profile & History: Tracks analysis history, user stats, and customizable detection settings.
🚀 Technical Architecture
The project is structured into two main layers: the User Interface (Frontend) and the ML Service (Backend/Model).
- Frontend (React + Tailwind CSS)
Single-File Application: Built as a highly portable, single-file HTML/JS application using in-browser Babel compilation.
UI Framework: React 18 for component-based architecture.
Styling: Tailwind CSS for rapid, responsive, and "Cyberpunk/Forensics" aesthetic design.
Data Visualization: Chart.js for real-time signal analysis graphs.
State Management: React Hooks (useState, useEffect) for managing application state (upload, scanning, history).
- Backend Services (Firebase Integration)
Authentication: Firebase Auth (Anonymous login support for seamless demos).
Database: Cloud Firestore for persistent storage of scan history, user profiles, and analysis results.
Storage (Planned): Firebase Storage or AWS S3 for handling large video file uploads.
- Machine Learning Pipeline (Planned Integration)
The UI is designed to interface with a Python-based ML backend (e.g., Flask/FastAPI) running the AVSFF model.
Visual Branch: CNN (Spatial Features)
Audio Branch: MFCC Extraction
Fusion & Classifier: BiLSTM alignment + Late Fusion for the final "Real vs. Fake" probability score.
🛠️ Setup and Usage
Prerequisites
A modern web browser (Chrome, Firefox, Edge, Safari).
An active internet connection (to load CDN dependencies like React, Tailwind, and Firebase).
Installation
No complex build steps (npm install, webpack) are required for the frontend prototype.
Download: Save the provided index.html file to your local machine.
Configure Database:
Create a project in the Firebase Console.
Enable Firestore Database and Authentication (Anonymous).
Copy your web app's configuration object.
Open index.html in a text editor and replace the firebaseConfig placeholder with your credentials.
Run: simply double-click index.html to open it in your browser.
Using the Application
Upload & Scan: Drag and drop a video file onto the upload zone. The system will simulate a scanning process (visualized with a progress ring).
Analyze Results: View the "Analysis Verdict" (Real/Fake), confidence score, and detailed breakdown (e.g., "Unnatural Blinking Pattern").
Review History: Navigate to the "History" tab to see a log of all past scans, fetched in real-time from the database.
Customize: Use the "Settings" tab to adjust detection sensitivity (High/Medium/Low) and toggle notifications.
📂 Project Structure
/deepfake-detection/ ├── index.html # Main application file (React + Tailwind + Firebase) ├── README.md # Project documentation └── /assets/ # (Optional) Static assets like icons or demo videos
💡 Future Roadmap
ML Backend Integration: Connect the frontend to a live Python API running the AVSFF model for actual inference.
Advanced Visualization: Implement real-time heatmap overlays on the video player using canvas manipulation.
Export Reports: Add functionality to generate PDF forensic reports for analyzed videos.
Multi-User Support: Expand the profile system to support secure login and team collaboration.
🤝 Contributing
Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.
License: MIT License