Deepfakes: Face Swapping and Deepfake Detection using Deep Learning Techniques

Abstract

Recent developments in deep learning have greatly improved the quality and capacity of creating photorealistic images and videos with advanced artificial intelligence algorithms and computer graphics; as a result, it is now more challenging to differentiate between authentic and fraudulent media. Although these artificial media have real-world applications, there could be a number of security and privacy concerns with them. One method that can result in these risks is deepfake. The terms “deep learning” and ”fake” are combined to form the term ”Deepfake”. Deepfakes can be used to replace another person’s face in a photograph or video, along with that person’s face along with their facial expressions. Leveraging deep learning and AI, Deepfakes leverage deep learning and AI to seamlessly replace facial identities and expressions, often undetectable by human observers. This study examines the various uses for deepfakes as well as their creation and detection method. It proposes a Face Swapping model for generating synthetic content by seamlessly transferring a source face onto a target photo or video while preserving the target’s facial movements as well as expressions. This paper analyzes the negative applications of Deepfakes that can harm and individual or the society. To address this challenge, we also introduce a model for detecting Deepfakes videos. We propose a hybrid Vision Transformer model for detecting Deepfake videos