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In the dynamic realm of deepfake detection, this work presents an innovativeapproach to validate video content. The methodology blends advanced2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model isuniquely tailored to capture spatiotemporal features via sliding filters,extending through both spatial and temporal dimensions. This configurationenables nuanced pattern recognition in pixel arrangement and temporal evolutionacross frames. Simultaneously, the 2D model leverages EfficientNetarchitecture, harnessing auto-scaling in Convolutional Neural Networks.Notably, this ensemble integrates Voting Ensembles and Adaptive WeightedEnsembling. Strategic prioritization of the 3-dimensional model's outputcapitalizes on its exceptional spatio-temporal feature extraction. Experimentalvalidation underscores the effectiveness of this strategy, showcasing itspotential in countering deepfake generation's deceptive practices.

Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles