A Comparative Analysis of Deepfake Detection Methods Using Overlapping Multiple Dynamic Images

Deepfake technology, which uses artificial intelligence to create realistic fake images, audio, and videos, has raised significant concerns due to its potential for misuse and manipulation. The emergence of deepfake technology poses a significant threat to the integrity of digital content, necessitating robust detection mechanisms. This paper proposes a novel method for deepfake detection by combining Overlapping Multiple Dynamic Images (OMDI) and Inversed Overlapping Multiple Dynamic Images (I-OMDI). Both representations capture temporal inconsistencies and subtle visual artifacts in fake videos by effectively utilizing spatial-temporal information. Our approach employs EfficientNetB7 as the backbone for feature extraction, enabling the model to distinguish between real and fake videos with high accuracy. By combining OMDI and I-OMDI with a weighted average strategy, we amplify the strengths of each method. Specifically, we assign equal weights of 0.5 to OMDI and I-OMDI based on their individual contributions to performance metrics. This balance yields substantial performance improvements across multiple datasets. When evaluated on the Celeb-DF v2 and DFDC datasets, our proposed model achieves state-of-the-art results, with an AUC score of 0.9952 on Celeb-DF v2 and 0.9947 on DFDC. These results underscore the robustness of the combined OMDI and I-OMDI methods in identifying deepfake videos. Furthermore, our model demonstrates superior performance compared to existing methods, including those by Tran et al. and Heo et al., underscoring its effectiveness in practical deepfake detection applications. (c) 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.