Multi-Modal Dynamic Fusion with Training-Free Generalisation for Integrated Deepfake Detection

The code is organized into different modules and branches to ensure a clear and reproducible workflow. It includes scripts for dataset preparation, model training, performance evaluation, and result generation, as well as the required model configurations and trained weights.

The project is designed to support transparency and reproducibility, allowing researchers to replicate the experiments by installing the required dependencies, preparing the dataset, running the training process, and evaluating the generated results. The main subject areas of this work include machine learning, artificial intelligence, deep learning, computer vision, and data science. No external funding was received for this research.