Deepfake Video Detection using Neural Networks

Abstract

In today’s era, software tools based on deep learning have made the people work easier to make credible faces exchanges in video with little signs of manipulation, nicknamed “DeepFake” videos. Manipulation in digital media has been performed for decades through the appropriate use of visual effects; nevertheless, current breakthroughs occurred in deep learning have resulted in a significant rise to gain reality of fake material or contents using the simple ways. This are Artifical Intelligence-generated media (known as DF). Using tools of artificial intelligence to create the DF is an easy task. However, detecting these DF poses a significant barrier. Because it is difficult to teach the algorithm to detect the DF. Using Convolutional Neural Networks and Recurrent Neural Networks, we have made progress in detecting the DF. The system employs a Convolutional Neural network (CNN) on frame level to extract features. These observations are noted and this can train a Recurrent Neural Network (RNN), which has the ability to learn and classify whether or not a video has been tampered with and identified the temporal irregularities in the frame introduced by DF tools. We demonstrate how utilizing a simple architecture, our system may get competitive outcomes in this job.