Comparative Analysis of Deepfake Video Detection Using Inception Net and Efficient Net

Human beings have the most distinctive feature that is human face. We can exchange somebody faces with anybody else's faces that appear realistic because many have another type of algo is based upon deepfake tech. Deepfake videos / photos is revolutionary subdual of AI tech by using someones human face can overwrite of someones face. More generously, with many different methods based on productive pictures. Unwillingly the overuse of smartphone and organizing by multiple internet web using AI manipulated data is reaching quicker in something which can we see in the 20th century, global danger is made up by these products Deepfakes are digital manipulation techniques that use machine learning to produce misleading videos. Identification is most difficult part to find from the original. Previously, CNN networks were used to perform identify the deep fake verification. Due to the increasing popularity of deep fakes identification of real one is more important find ways to detect manipulated videos that are presented as real ones. In this project, we will study different methods that can be used to detect such images as well as videos. This study shows that they can also be done using a convolutional algorithm known as Efficient Net and Inception Net. In this Paper, we compare various versions of Convolutional Inception Net with various versions of convolutional Efficient Net combined with Vision Transformers and different Data files to obtain best possible results in Deepfake detection. To get the highly accurate percentage to identify the video is fake or real by using efficient net and by inception net. tract)