XDF: A Large-Scale Dataset for Evaluating Video Deepfake Detection Across Multiple Manipulation Techniques

Abstract

Deepfake technologies have rapidly advanced, presenting significant challenges to the integrity of digital media and creating potential risks in various sectors, from politics to personal privacy. In response, the research community has focused on developing reliable deepfake detection methods. However, the continuous advancements and growing complexity of artificial intelligence (AI) models have outpaced existing datasets, making it difficult to train and evaluate detection systems effectively. This paper addresses this gap by introducing a comprehensive dataset of real and manipulated videos, aimed at supporting the development and evaluation of advanced deepfake detection models. This dataset could serve as a benchmark for assessing the effectiveness of emerging detection methodologies, providing a standardized resource for researchers to measure progress and compare results. Additionally, this study offers key insights for the creation of effective deepfake datasets and identifies several pressing challenges in the field, guiding future research and development efforts.