  In the dynamic realm of deepfake detection, this work presents an innovative
approach to validate video content. The methodology blends advanced
2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model is
uniquely tailored to capture spatiotemporal features via sliding filters,
extending through both spatial and temporal dimensions. This configuration
enables nuanced pattern recognition in pixel arrangement and temporal evolution
across frames. Simultaneously, the 2D model leverages EfficientNet
architecture, harnessing auto-scaling in Convolutional Neural Networks.
Notably, this ensemble integrates Voting Ensembles and Adaptive Weighted
Ensembling. Strategic prioritization of the 3-dimensional model's output
capitalizes on its exceptional spatio-temporal feature extraction. Experimental
validation underscores the effectiveness of this strategy, showcasing its
potential in countering deepfake generation's deceptive practices.
