Hybrid Deepfake Detection Using CNN for Spatial Analysis and LSTM for Temporal Consistency
Lakshmi Venkata Manikanta Maguluri, Hema Naga Vamsi Kothamasu, Shiny Duela Johnson · 2025
Deepfake technology, driven by advancements in artificial intelligence, enables the creation of highly realistic manipulated videos, posing significant threats to security, privacy, and misinformation. Traditional detection methods struggle to keep pace with the evolving sophistication of deepfake techniques. This study proposes a hybrid deep learning approach that leverages Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence analysis to enhance deepfake detection accuracy. The CNN model captures spatial inconsistencies and artifacts in individual frames, while the LSTM network analyzes sequential dependencies to detect temporal anomalies indicative of deepfakes. Experimental evaluations on benchmark datasets demonstrate the effectiveness of the approach, achieving high accuracy in distinguishing real from fake videos. The proposed model offers a robust and scalable solution for deepfake detection, contributing to the fight against digital media manipulation and misinformation.