Deepfake Video Detection Using Deep Learning

Abstract

The exponential growth of deep learning techniques has led to the development of highly realistic synthetic media known as deepfakes. These manipulated videos convincingly replicate facial expressions, speech patterns, and visual char- acteristics, making them difficult to distinguish from authentic content. While deepfake technology has potential applications in entertainment and digital art, its misuse presents severe threats including misinformation, identity fraud, reputational damage, and cybercrime. Conventional detection techniques relying on manual inspection or handcrafted features have proven ineffective against sophisticated deepfake generation models.

This paper proposes an automated deepfake video detection framework based on deep learning methodologies that analyze both spatial and temporal inconsistencies within video sequences. The proposed system integrates Convolutional Neural Networks (CNN) for extracting spatial features from facial regions and Long Short-Term Memory (LSTM) networks for modeling tem- poral dependencies across video frames. The system identifies subtle artifacts such as unnatural facial texture, blending er- rors, inconsistent eye movements, and discontinuities in motion patterns. The dataset used in this study consists of real and manipulated videos collected from benchmark sources including FaceForensics++ and DFDC. Experimental evaluation demon-strates that the proposed hybrid CNN-LSTM approach effectively differentiates deepfake videos from authentic ones with high reliability, contributing to enhanced digital media verification and forensic analysis.

KEYWORDS: Deepfake Detection, Deep Learning, CNN, LSTM, Video Forensics, Artificial Intelligence, Media Authentication