Leveraging Transfer Learning for Region-Specific Deepfake Detection
Sim Wei Xiang Calvert, Pai Chet Ng · 2024
Deepfake technology, which utilizes advanced artificial intelligence to create or manipulate multimedia content, presents significant challenges by obscuring the distinctions between reality and fiction. This phenomenon can lead to severe consequences such as misinformation and deception, particularly in culturally diverse regions like Southeast Asia. In response, this paper aims to enhance deepfake detection capabilities specifically for the Southeast Asian context, with a focus on Singapore, utilizing the Trusted Media Challenge (TMC) dataset. We employ transfer learning to fine-tune existing models with region-specific data and explore various layer freezing strategies to optimize performance. Additionally, we assess the effectiveness of transfer learning against the complete retraining of models to identify the most resource-efficient practices for improving detection capabilities. Our findings reveal that targeted fine-tuning of specific layers can enhance model performance in identifying regional deepfake, providing a balance between computational efficiency and detection accuracy. This research contributes to the development of robust, region-specific deepfake detection methods, which are crucial for combating the evolving threats posed by deepfake technology. We have developed a web application using our trained model, the web application and the source code are available at https://github.com/ict-at-sit/deepfake-detection-app.