Deepfake Video Detection Using Res-Next CNN and LSTM

Abstract

Abstract: Deepfake videos are becoming more and more common in the digital age, which has led to major worries about how they can undermine the trustworthiness of visual media. With deep learning algorithms' rising processing power, producing lifelike human-synthesized films or deepfakes has never been easier. Disinformation and political unrest can be caused by these videos. A novel deep learning-based method has been created to distinguish between actual and AI-generated fraudulent videos to address this problem. The suggested technique uses Attention-based networks (Res-Next CNN), a kind of deep learning architecture that can selectively focus on significant features in a video, to fine-tune the transformer module to search for new sets of feature space to detect false images.