DEEPFAKE DETECTION USING DEEP LEARNING

Abstract

Deep learning algorithms have become as powerful as computation power has increased that creating an indistinguishable human synthesized video, also known as deep fakes, has become very simple. Deepfakes involves images and videos, often obscene, in which a face can be swapped with someone else’s which can be used to create political distress, criminal activities, revenge pom, and blackmailing people as individuals as well as communities and organizations. In this project, we have described a deep learning method that can effectively distinguish between a fake or Al-based generated video from a real video. The method used in this project comprises of a Res-Next Convolution neural network to extract the frame-level features in the first phase and for the second phase these features are further used as an input to train the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) to classify the videos as fake or real. To make the model perform better on real time data, we have created a new dataset, by combining various datasets like FaceForensic-H-, Face Forensics in the Wild (FFIW), Deepfake detection challenge, and Celeb-DF. The evaluated results show the effectiveness of the proposed method comparing with existing models like VGG-16 and CapsureForensics on the newly created dataset