Deepfake Video Face Detection using Deep Learning

Abstract

The proliferation of deepfake technology, which uses artificial intelligence to create highly realistic synthetic videos and images, poses major risks to privacy, security, and confidence in digital platforms. Traditional approaches to achieving these properties are often limited by the complexity of the algorithms. This paper proposes a novel approach for deepfake face detection using Deep Learning (DL) suited for sequential data analysis. Our method leverages the temporal dependencies and patterns inherent in video sequences to identify subtle inconsistencies and artifacts introduced by deepfake generation processes. By analyzing frames in a sequence rather than in isolation, the DL model captures dynamic facial features and movements that are challenging to replicate accurately in deepfakes. The proposed model is trained on a comprehensive dataset of real and deepfake videos, incorporating various scenarios and manipulation levels. Experimental results demonstrate that our DL-based approach achieves superior accuracy and robustness compared to the latest developments deepfake detection techniques, particularly in challenging cases with high-quality deepfakes. Furthermore, the model exhibits strong generalization capabilities across different datasets and deepfake generation methods. This research highlights the potential of DL for enhancing deepfake content detection, contributing to innovations in technology are advancing steadily secure and trustworthy digital media platforms.