Enhancing Deepfake Detection Through Innovative Data Augmentation Strategies and Frame-Based Deep Learning Architecture

The development of deepfake technology underscores the imperative of deepfake detection research. A comprehensive evaluation spanning diverse detection approaches and datasets illuminates the vulnerability of current models to a spectrum of deepfake types, revealing a notable deficiency in their adaptability to emerging variations and their efficacy in identifying novel threats. This paper focuses on the assessment of deepfake detection models across different datasets. We explore the enhancement of performance achieved through the integration of diverse datasets during training. Furthermore, we introduce a novel video deepfake detection strategy grounded in frames processing, thereby augmenting result recall and aligning with practical application requirements. The experimental results show a significant improvement in deepfake detection performance and confirm the high adaptability and reliability of the proposed detection models.