Fake Face2Face Video Detection Using a Novel Scene and Texture Based Feature Set

The existence of fake videos is a problem that is challenging today's social media-enabled world. There are many classifications for fake videos with one of the most popular being Face2Face. Detecting such fake videos is a challenging issue. This research attempts to comprehend the characteristics that belong to Face2Face videos. In attempting to understand Face2Face videos this work investigates the characteristics of the video that make them unique. As such this research uses scene and texture detection to develop a unique feature set containing 19 data features which is capable of detecting whether a video is a Face2Face or not. This study validates the feature set using a standard dataset of the features relating to the characteristics of the video. These features are analysed using a classification machine learning model. The results of these experiments are examined using four evaluation methodologies. The analysis reveals positive performance with the use of the ML method and the feature set. From these results, it can be ascertained that using the proposed feature set, a video can be predicted as a Face2Face or not and as such prove the hypothesis that there exists a correlation between the characteristics of a video and its genuineness, i.e., whether or not a video is a Face2Face.