To make the proposed framework more adaptable to handle emerging forgery methods in the future, a few strategies can be implemented:
Continuous Dataset Updates: Regularly updating the dataset used for training the model with samples of the latest deepfake techniques can help the model stay current with emerging trends in forgery methods. This ensures that the model is exposed to a diverse range of deepfake variations and can adapt to new challenges.
Transfer Learning: Implementing transfer learning techniques can enable the model to leverage knowledge gained from detecting existing forgery methods to quickly adapt to new ones. By fine-tuning the pre-trained model on a smaller set of data related to the new forgery methods, the model can learn to detect emerging deepfakes more efficiently.
Ensemble Learning: Employing ensemble learning, where multiple models are combined to make predictions, can enhance the model's adaptability. By training multiple models with different architectures or on different subsets of data, the ensemble can collectively make more robust predictions and better handle variations in emerging forgery methods.
By incorporating these strategies, the proposed framework can be made more adaptable and resilient to the evolving landscape of deepfake generation techniques, ensuring its effectiveness in detecting new and emerging forms of deepfakes.