Deepfake Detection Based on Temporal Analysis of Facial Dynamics Using LSTM and ResNeXt Architectures

Abstract

The proliferation of deepfake technology presents a critical challenge to the authenticity and trustworthiness of digital media. To address this issue, we propose an innovative deepfake detection framework that combines the power of Long Short-Term Memory (LSTM) and ResNeXt architectures. By integrating spatial and temporal analysis methods, our approach aims to accurately identify manipulated videos amidst the vast sea of online content. Through rigorous experimentation and evaluation using diverse datasets, our framework demonstrates promising results in effectively distinguishing between genuine and fake videos. This research contributes to the ongoing efforts to combat deepfake misinformation and uphold the integrity of digital media platforms.