Evaluating Features and Variations in Deepfake Videos Using the CoAtNet Model

Deepfake video detection has emerged as a critical challenge in the realm of artificial intelligence, given its implications for misinformation and digital security. This study evaluates the generalisation capabilities of the CoAtNet model-a hybrid convolution-transformer architecture-for deepfake detection across diverse datasets. Although CoAtNet has shown exceptional performance in several computer vision tasks, its potential for generalisation in cross-dataset scenarios remains underexplored. Thus, in this study, we explore CoAtNet's generalisation ability by conducting an extensive series of experiments with a focus on discovering features and variations in deepfake videos. These experiments involve training the model using various input and processing configurations, followed by evaluating its performance on widely recognised public datasets. To the best of our knowledge, our proposed approach outperforms state-of-the-art models in terms of intra-dataset performance, with an AUC between 81.4% and 99.9%. Our model also achieves outstanding results in cross-dataset evaluations, with an AUC equal to 78%. This study demonstrates that CoAtNet achieves the best AUC for both intra-dataset and cross-dataset deepfake video detection, particularly on Celeb-DF, while also showing strong performance on DFDC.