Deepfake Detection Using Deep Learning

Abstract

Artificial intelligence is being used to create hyper-realistic synthetic media as well as misinformation, identity theft, and fraud. As deepfake techniques become more sophisticated, deepfake detection becomes more crucial. This paper explores the application of deep learning approaches for the detection of deepfakes. A deep learning model can distinguish between authentic and manipulated media based on experimental results. The various methods for detecting deepfake videos and images are assessed using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. It discusses recent developments in the field, highlights challenges in deepfake detection, and proposes potential methodologies to achieve high accuracy. A deep learning model can distinguish between authentic and manipulated media based on experimental results. The article concludes with a discussion of the limitations of current methods, as well as recommendations for future research.