Multimedia Deepfake Detection Using Deep Learning

Multimedia Deepfake Detection Using Deep Learning

Prof. Prashant Raut1, Ramkrishna Masan2, , Sangram Gangane3 , Arya Deopurkar4, Karan Sasane5

Department of Computer Engineering,

K J College of Engineering and Management Research, Pune, India.

*Corresponding e-mail: 1 prashantraut.kjcoemr@kjei.edu.in, 2masan.ramkrishna1104@gmail.com ,   3sangramgangane@gmail.com , 4aryadeopurkar17@gmail.com, 5karansasane37@gmail.com

Abstract – The spread of deepfake technology poses significant challenges for sectors that rely on the verification of digital images, such as journalism. law enforcement and financial institutions. Deepfakes are highly realistic artificial datasets sent using deep learning techniques. making it more and more difficult To distinguish real objects from synthetic content. This article reviews recent advances in Deepfake detection, focusing on the methods used. Convolutional Neural Network (CNN) and explores the strengths and limitations of various CNN architectures and detection methods. It also presents problems with a wide range of data types. Resistance to opponent attacks and efficiency in calculations Based on a comprehensive review of papers published in the past 3 years. This paper aims to provide a comprehensive understanding of CNN-based deepfake detection techniques, providing insights into current trends and possible future directions in CNN work.

Keywords: Deep effect, Detection, Deep learning, Image, Accuracy.