Spatiotemporal and Behavioral Feature-Aware Model with XceptionCapsule Fusion for Deepfake Detection

Deepfake detection continues to pose significant challenges, primarily because existing methods often suffer from key limitations, including reliance on individual frame analysis, vulnerability to low-resolution or compressed videos, and inability to capture temporal inconsistencies. Furthermore, traditional face detection techniques frequently fail under challenging conditions such as poor lighting or occlusion, while many models struggle with subtle manipulations due to inadequate feature extraction and overfitting on limited datasets. To address the drawbacks of existing deepfake detection approaches, this research proposes a Face and Motion-Aware Detection Framework that integrates both spatial and temporal information. The framework begins with a preprocessing stage that extracts video frames at a fixed rate to ensure temporal consistency. Facial regions and detailed landmarks are accurately detected using BlazeFace and MediaPipe Face Mesh. These features are then processed by the proposed XceptionCapsule Net, which combines the spatial feature extraction capabilities of the Xception model with the hierarchical and viewpoint-aware representation of Capsule Networks (CapsNet), and the temporal dependency modeling power of a Bidirectional Long Short-Term Memory (BiLSTM) layer. The architecture incorporates Global Average Pooling, Flatten, and fully connected layers, with Sigmoid activation for binary classification. Extensive evaluations on the FaceForensics++ (FF++) and Celeb-DF datasets demonstrate strong performance, achieving up to 99.31% accuracy and 99.99% Area Under the Curve (AUC). The results validate the framework’s effectiveness, precision, and generalization across various video qualities and manipulation scenarios.