A Review on Deepfake Detection
The goal of the research is to create a deep learning-based method for identifying deepfake videos that can differentiate between real and fake information by using deep learning techniques and a variety of datasets for training, which helps combat the proliferation of false visual media.
Abstract
Deepfake video detection is a new field in artificial intelligence (AI) and computer vision. Its main objective is to detect deepfake videos, which are digitally altered footage in which the original video is replaced with that of another person. "Deepfake video detection" is the process of recognizing and labelling videos that have been created by altering or substituting the appearance and actions of persons in the video through the use of deep learning techniques. These techniques are often used to create extremely realistic fake videos that can be used for deceptive purposes, such as spreading false information or assuming the identity of another individual. Deepfake videos are distinguished from real ones by examining patterns in the video footage and looking for differences in facial expressions. Our initiative is to protect the legitimacy of visual media in the digital age by making a substantial contribution to the fight against the proliferation and misuse of deepfake materials. The goal of the research is to create a deep learning-based method for identifying deepfake videos. Our system can differentiate between real and fake information by using deep learning techniques and a variety of datasets for training, which helps combat the proliferation of false visual media. Conclusively, our effort on deep learning-based deepfake video identification is an essential step towards tackling the escalating danger of digital manipulation. Key Words: Deepfake, Artificial Intelligence (AI), Deep Learning, Video Analysis, Facial Recognition, Video Authentication, Detection.