A Systematic Literature Analysis on the Fake Identification

Rapid progress in AI, machine learning, and deep learning over the last few decades has resulted in new methodologies and tools for altering multimedia. Though the technology has generally been utilized for respectable causes such as entertainment and education, rogue people have also used it for illegal or evil purposes. High-quality and realistic fake movies, photos, or audios, for example, have been generated to disseminate disinformation and propaganda, incite political division and hatred, or even harass and blackmail people. Deepfake is a term that refers to modified, high-quality, realistic videos. To address the issues posed by Deepfake, many ways have been discussed in the literature. In this study, we undertake a systematic literature review (SLR) to offer an updated overview of the research efforts in Deepfake detection, summarizing 112 relevant papers from 2018 to 2020 that provided a range of techniques. We classify them into four categories: deep learning-based approaches, traditional machine learning-based methods, statistical techniques, and blockchain-based techniques. We also compare the detection capabilities of the various algorithms across different datasets and find that deep learning-based methods outperform other methods in Deepfake detection.