Efficient Spatiotemporal Deepfake Video Detection using a Lightweight CNN–LSTM Framework with Temporal Attention

Abstract

Deepfake videos generated using modern deep learning techniques pose significant threats to digital media authenticity and public trust. These manipulated videos often appear highly realistic, making manual verification difficult. This paper proposes an efficient spatiotemporal deepfake detection framework that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a temporal attention mechanism. MobileNetV2 is utilized as a CNN model for spatial feature extraction in individual video frames. The extracted features are then subjected to the LSTM network for detection of temporal inconsistencies in the video frames. Furthermore, the use of the temporal attention mechanism is proposed for the identification of video frames where the level of manipulation is higher. The experimental results show the efficiency and accuracy of the proposed method in video forgery detection. The proposed method attains 96.1% detection accuracy and has less computational complexity compared to other methods. Ablation experiments confirm the contribution of each component, and robustness is evaluated under H.264 compression and adversarial perturbation conditions.