A reliable solution to detect deepfakes using Deep Learning

Abstract

Recently, it has become simple to produce trustworthy face video exchanges that leave a few signs of deception thanks to in-depth free reading software tools (DF). Despite decades of effective use of visual effects in digital video deception, recent developments in in-depth learning have significantly improved the genuine nature of misleading content and the accessibility that can be achieved with it. This is referred to as AI-synthesized media or DF in short. Making DF is a simple task that uses practical tools. However, it is a significant difficulty if these DFs are discovered, because it is hard to train the algorithm for identifying DF. CNNs and RNNs have helped us come closer to DF. The Convolutional Neural Network (CNN) is used by the system to extract features at the individual level. The continuous neural network (RNN) states learn to recognize whether or not a video is being deceived and be able to spot temporary anomalies among the frames given by DF's creative tools thanks to these capabilities. An extensive collection of pseudo-videos gathered from a common data source is the anticipated outcome. We demonstrate how our method can produce a competitive outcome in this work that is simple to utilize.