Unmasking Deepfakes: Using Resnext and LSTM to Detect Deepfake Videos

Abstract

This paper proposes an approach for detecting deepfake videos using Resnext CNN and LSTM. The proposed approach involves training a Resnext CNN on a dataset of real and deepfake videos to classify them accurately. The Resnext CNN takes video frames as input and outputs a probability score for each frame, which is then fed into an LSTM to model the temporal dynamics of the video. The approach was evaluated on a dataset of real and deepfake videos and achieved promising results. The proposed approach can be used to detect deepfake videos, which can help in preventing the spread of misinformation and safeguarding our society.