FakeCheck: AI-Powered Deepfake & AI Generated Video Detection Platform

This repository contains the source code for FakeCheck, a full-stack application designed to detect deepfakes and AI-generated video content. It features a React/TypeScript frontend and a Python/FastAPI backend that work together to provide a seamless user experience.

Overview

The platform allows users to upload a video file, which is then analyzed by a sophisticated pipeline on the backend. The analysis involves multiple AI models and heuristic checks to identify visual artifacts, lip-sync issues, abnormal blinking, and other tell-tale signs of manipulation. The results are then presented in a detailed, user-friendly report on the frontend.

Architecture

• Backend: A powerful FastAPI-based REST API that orchestrates the fake video detection pipeline. It uses CLIP for visual authenticity scoring, Whisper for audio transcription, and Google's Gemini Pro for advanced visual and audio analysis.
• Frontend: A modern, responsive web application built with React, TypeScript, and Tailwind CSS. It provides the user interface for uploading videos and viewing the analysis results.

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python 3.10
• Node.js v18+
• FFmpeg: (brew install ffmpeg on macOS or sudo apt-get install ffmpeg on Debian/Ubuntu)
• API Keys:
  • Google Gemini API Key
  • HuggingFace Token (for model downloads)
  • Optional: Google Cloud Credentials - A JSON key file for a service account with the Video Intelligence API enabled. Only add if you enable Google Video Intelligence lighting shift detection.

Getting Started

Follow these steps to set up and run the entire application locally.

1. Clone the Repository

git clone https://github.com/mixpeek/fake-check.git
cd FakeCheck

2. Backend Setup

First, let's get the backend server running.

1. Navigate to the backend directory:

   cd backend

2. Create and activate a Python virtual environment:

   python3.10 -m venv venv
   source venv/bin/activate
   # On Windows, use: venv\Scripts\activate

3. Install Python dependencies:

   pip install -r requirements.txt

4. Configure Environment Variables: Copy the example .env file and fill in your API keys.

   cp .env.example .env

   Now, edit the .env file with your credentials:

   GEMINI_API_KEY="your_gemini_key_here"
   HF_TOKEN="your_huggingface_token_here"
   GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/google-cloud-key.json" # Optional: Disabled by Default

5. Run the Backend Server:

   python run_server.py

   The backend API will now be running at http://localhost:8001.

3. Frontend Setup

With the backend running, open a new terminal window and set up the frontend.

1. Navigate to the frontend directory:

   # From the project root
   cd frontend

2. Install NPM dependencies:

   npm install

3. Run the Frontend Development Server:

   npm run dev

   The frontend application will be available at http://localhost:5173. It is pre-configured to connect to the backend API running on http://localhost:8001.

You can now open http://localhost:5173 in your browser to use the application.

Full Fake Video Detection Pipeline