jayadeepikareddy1-hub/deepfake-detector-

# Deepfake & Certificate Authenticity Detector A sophisticated AI-powered system for detecting deepfakes in media and verifying the authenticity of certificates. ## 🚀 Features - **Deepfake Detection**: Analyze images and videos to detect AI-generated faces and synthetic pixel artifacts. - **Certificate Verification**: - **OCR Analysis**: Extracts and validates structural keywords for various document types (Educational, Birth, Identity, Medical). - **Visual Forensic Analysis**: Uses Error Level Analysis (ELA) and a fine-tuned Vision Transformer (ViT) model to detect logo splicing and text tampering. - **Grayscale Optimization**: Uses CLAHE (Contrast Limited Adaptive Histogram Equalization) to expose hidden artifacts in black & white documents. - **Unified Dashboard**: Modern React frontend for easy file uploads and detailed analysis reports. ## 🛠️ Technology Stack - **Backend**: FastAPI, PyTorch, Hugging Face Transformers (ViT), OpenCV, Tesseract OCR. - **Frontend**: React, TailwindCSS (for styling), Axios, Lucide-React. - **Models**: Fine-tuned `prithivMLmods/Deep-Fake-Detector-v2-Model` for both faces and document tampering. ## 📋 Installation ### Backend 1. Navigate to the `backend` directory. 2. Install dependencies: pip install -r requirements.txt 3. Ensure Tesseract OCR is installed on your system (default path: `C:\Program Files\Tesseract-OCR\tesseract.exe`). ### Frontend 1. Navigate to the `frontend` directory. 2. Install dependencies: npm install ## 🏃 Running the Project 1. **Start Backend**: python backend/main.py 2. **Start Frontend**: npm run dev ## 🧠 Training To fine-tune the models on your own dataset: 1. Place images in `dataset/images` or certificates in `dataset/certificates/extract`. 2. Run the training script: python backend/train.py ## 📄 License This project is for educational and research purposes.