| # Deepfake Image Detection Tool Using Xception Architecture | |
| This project is a web-based Deepfake Image Detection Tool developed for Apex Broadcasting Network Ltd to verify the authenticity of digital images before publication. The system uses a deep learning model based on the Xception architecture to accurately distinguish between real and manipulated images. | |
| ## Features | |
| - Image upload and deepfake detection | |
| - Xception-based deep learning detection engine | |
| - Confidence score for each prediction | |
| - Deepfake literacy and awareness content | |
| - Secure image handling with CSRF protection and rate limiting | |
| ## Technology Stack | |
| - Backend: Python (Flask) | |
| - Frontend: HTML, CSS, JavaScript | |
| - Deep Learning Model: Xception (TensorFlow/Keras) | |
| - Security: CSRF protection, rate limiting, secure headers | |
| ## Project Structure | |
| - `app.py` – Flask backend and detection logic | |
| - `templates/index.html` – User interface | |
| - `static/` – Images and frontend assets | |
| - `best_xception_model_finetuned.keras` – Trained model | |
| - `uploads/` – Temporary image storage | |
| ## How to Run Locally | |
| ```bash | |
| pip install -r requirements.txt | |
| python app.py | |
| "# deepfake-tool" | |