Study on Transfer Learning Technique for Deepfake Face Detection Using Weighted Average Ensemble Model

Abstract

Deepfakes represent a significant cybersecurity threat with their ability to create highly convincing fraudulent media. As deepfake technology becomes more sophisticated and accessible, the potential for cybercrimes such as identity theft, fraudulent account openings, and financial scams increases. To address the rising threat of deepfakes, this research explores detecting deepfake face images by combining transfer learning with an ensemble technique. Four pre-trained models have been employed for the transfer learning task. Finally top three performing models were combined for the ensemble. The ensemble model has been evaluated against a benchmark dataset, namely 140K Real and Fake Faces. The ensemble model significantly surpassed the individual models, achieving an accuracy of 81.25%. This research demonstrates the potential of integrating multiple pre-trained models to improve deepfake image detection, laying a strong foundation for future advancements.