Hybrid CNN-LSTM Architectures Based on Attention Mechanism for Deepfake Video Detection using Spatiotemporal Chromatic Energy Distributions

The continuous growth of AI-generated synthetic media poses significant threats, including identity fraud, misinformation, and political manipulation, as deepfake video becomes increasingly indistinguishable from genuine recordings. The core problem is that these synthetic videos are often indistinguishable from authentic content to the human eye, rendering conventional forensic methods ineffective. This study therefore has the primary objective of developing a robust, intelligent detection mechanism capable of identifying subtle spatiotemporal artifacts in manipulated videos. The goal is to design and evaluate a hybrid deep learning architecture that leverages the complementary strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling, enhanced by an attention mechanism to focus on the most discriminative video segments. A key innovation of this work is the use of Spatiotemporal Chromatic Energy Distributions (SCED) as input features, which effectively model the harmonic relationships in authentic video and are sensitive to the inconsistencies introduced by synthetic generation processes. The results demonstrate the superior performance of the proposed hybrid CNN-LSTM model with attention, which achieved an accuracy of 98.7%, a precision of 96.2%, a recall of 94.4%, and an F1-score of 96.7%, significantly outperforming standalone CNN (88.5% accuracy) and LSTM (91.4% accuracy) baselines. In conclusion, this research confirms that the integration of SCED features with a hybrid CNN-LSTM architecture and attention mechanism provides a highly effective and reliable solution for deepfake video detection, offering a powerful tool to enhance the security and trustworthiness of digital media.