JoWUA

Abstract

The rapid evolution of Generative Artificial Intelligence (AI) has significantly increased the prevalence of high-fidelity deepfakes, posing a severe threat to digital security and social media integrity. Existing detection frameworks often struggle with generalization and the identification of multi-scale spatio-temporal artifacts. This research addresses these challenges by proposing a robust detection system utilizing the Deep Inception V4 architecture integrated with Predictive Representation Learning. By leveraging multi-scale convolution kernels and specialized reduction blocks, the proposed model extracts both microscopic pixel-level inconsistencies and macroscopic structural anomalies commonly found in manipulated videos. The methodology was rigorously evaluated using the large-scale Deepfake Detection Challenge (DFDC) dataset. Experimental results demonstrate that the Inception V4 model achieves a Training Accuracy of 94.87% and a Validation Accuracy of 92.74%, representing a substantial improvement over baseline CNN (82.00%) and VGG-19 (80.00%) architectures. Statistical validation using a two-sample t-test yielded a p-value < 0.001, confirming the significance of these performance gains. Furthermore, the model achieved a Receiver Operating Characteristic (ROC-AUC) of 0.96, indicating high precision and recall in identifying 2,308 true positive cases within the test subset. These findings conclude that multi-scale feature extraction, combined with self-supervised pre-training, provides a superior defense against generative threats. Future work will investigate the integration of lightweight hybrid transformers to enhance real-time detection on mobile platforms while maintaining high classification sensitivity.