Hierarchical Multi-Branch Deepfake Detection Network (HMBDDN)

Deepfake technology, which manipulates visual media, poses significant threats to content authenticity and security. This paper addresses these challenges by presenting a novel deepfake detection system designed to enhance detection accuracy, mitigate data imbalance, and improve generalization capabilities. Our approach encompasses multiple stages, including robust face detection, comprehensive feature extraction, effective data balancing, and precise classification, providing a holistic solution. Specifically, the system extracts intricate features from facial data and employs an advanced data balancing strategy to ensure equitable training across classes. A sophisticated discriminator model is then utilized to perform the final classification. The proposed method achieves high accuracy on the Celeb-DF v2 dataset, outperforming existing state-of-the-art (SOTA) deepfake detection models. These results demonstrate the robustness and effectiveness of our approach, contributing to more reliable detection of manipulated media and enhancing the integrity of digital content.