Abstract:

The primary objective of this study is to rigorously evaluate the effectiveness of these deepfake detection algorithms, utilizing multiple key performance metrics, including accuracy, precision, recall, and F1-score. The focus of the analysis is centered on their ability to differentiate between authentic and manipulated videos. Furthermore, the research delves into a more granular examination of specific deepfake manipulation types, aiming to uncover variations in detection accuracy and performance across these categories. The study goes beyond algorithmic analysis and explores how dataset characteristics, such as diversity and size, influence the detection performance of the tested algorithms. The outcomes of this research are anticipated to make significant contributions to the advancement of deepfake detection technology. Moreover, the insights gained from this study will not only aid in refining existing detection algorithms but also provide valuable guidance for future research in the field of deepfake detection, ultimately contributing to the ongoing battle against the proliferation of deceptive digital media.