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.

该研究使用 2D 和 3D 卷积神经网络的创新方法验证视频内容，结合滑动过滤器捕捉空时特征，利用 Voting Ensembles 和 Adaptive Weighted Ensembling 优化 3D 模型输出，在对抗深度伪造生成的欺骗性实践方面展现出潜力。