A Survey on Detecting Deep Fakes Using Advanced AI-Based Approaches

Abstract

Today, artificial intelligence is used to clone human faces, which leads to a new technology known as “deepfakes.” Recently, machine learning (ML) approaches and the use of deep learning (DL) networks have captured researchers' competition to achieve the highest classification accuracy in building efficient models for digital content deepfake detection. Therefore, this review analyzes and compares existing deepfake detection methods based on advanced artificial intelligence algorithms. Thus, deepfake detection techniques were classified into three major categories based on the classifier model used (machine learning, deep learning, or hybrid) and then compared to show the aspects that influence the efficiency and accuracy of the algorithms. This research helps researchers develop efficient classification models for deepfake detection applications. Based on the survey information reviewed in this study, a discussion of open issues and future directions is presented. The most important challenges and research directions related to deepfake detection methods are discussed.