Overview The aim of this project is to build a machine learning model that can classify images as either real or fake faces. This project involves: Data Collection and Preprocessing: Collecting a dataset of real and fake faces. Preprocessing images to make them suitable for training.
Model Building: Designing and training a Convolutional Neural Network (CNN) for classification.
Evaluation: Testing the model's performance on unseen data (created by splitting the exiting dataset).
Deployment: Making the model accessible via a simple interface (future work).
Features Detects real vs fake faces with a CNN-based classifier. Preprocessing pipeline for cleaning and preparing image data. Performance metrics including accuracy.
Requirements To run this project, you need: Python 3.8+ TensorFlow keras NumPy Matplotlib
Feedback & Contributions As a student project, I'm still learning! If you have suggestions, find issues, or want to contribute, feel free to: Open an issue. Submit a pull request. Reach out to me directly.
You can find the dataset used here: https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection?resource=download
Thank you for visiting! ⭐