A Robust Framework for Deepfake Detection Using Advanced Neural Architectures and Generalization Techniques

The emergence of deepfake technology has created major obstacles to the quality of digital media and presented major hazards to privacy, security, and public trust. To achieve high speed and scalability, we present a robust deepfake detection system in this paper that blends lightweight Convolutional Neural Network (CNN) architecture with effective preprocessing. Using MTCNN-based face identification, the system accurately isolates and aligns facial regions from video frames, reducing noise while improving feature extraction. To improve model generalization, preprocessing techniques include dynamic frame sampling and data augmentation techniques like color correction and geometric adjustments. The proposed CNN architecture, consisting of three convolutional blocks, dropout regularization, and fully linked layers, enables efficient feature extraction and the identification of genuine or fake facial images. For experimental evaluations, the Deepfake Detection Challenge dataset was utilized. The model achieved 93.5% accuracy and 0.38 validation loss, surpassing state-of-the-art methods like XceptionNet and MesoNet. These results show how the proposed method can detect deepfake changes in various video scenarios while preserving computational efficiency.