A real-time deepfake image detection system built with a custom deep learning model trained from scratch. Users can upload images through an intuitive Streamlit interface to classify them as real or AI-generated/manipulated. This application represents the culmination of advanced research in computer vision and machine learning, specifically designed to stay ahead of evolving deepfake generation techniques. The deepfake media circulating on the social media applications can be checked using this tool. This application is built on groundbreaking research that combines two powerful model architectures:
- GANs: GANs used for sophisticated deepfake creation.
- ResNet: ResNet for feature extraction and analysis.
Check out the code of the project by clicking here!
- Custom-built deep learning model for binary classification (Real/Fake)
- User-friendly web interface powered by Streamlit
- Real-time image analysis and prediction
- Model stored in .pt format for efficient deployment
Clone the repository to your local machine:
git clone https://github.com/TarunSehgal27/Deepfake-Image-Detection.git
Install the required dependencies:
pip install -r requirements.txt
Run the Streamlit app:
streamlit run app.py
- Frontend: Streamlit
- Backend: Python
- Deep Learning Framework: Pytorch