Scrutinization of Video posts on Social Media for Authenticity

Abstract

Multimedia posts, especially video posts are easy to understand, attractive and are considered as proof of evidence for an event that occurred. Videos are one of the frequently shared posts on social media. Videos and images speak louder than words. They are very much believed by people and forwarded. Videos reach every person despite of language or literacy barriers. Technological improvements lead to easy generation of fraudulent videos to create a negative impact on people and society. Celebrities and politicians are highly affected because of fake video generation and dissemination. The video posts uploaded and forwarded on social media should be analyzed and identified to stop propagation before they create harm to the society. The proposed work converts the video clips into a sequence of frames. The keyframes are then identified by the use of histogram comparison. A CNN model is built with optimum layers for image classification. The keyframes or images are then classified using the CNN model to identify fraudulent content. The proposed work is light in terms of processing when compared to existing or conventional video classification. The work uses FaceForensics++, DeepFake and Celeb-DF V2 datasets and achieved 99.7%, 99.8% and 98.01% accuracy to identify fraudulent video posts.