The Creation and Detection of Deepfakes: A Survey
It addresses the problem of misinformation and malicious use of deepfakes for researchers and practitioners, but is incremental as it synthesizes existing knowledge.
This survey explores the creation and detection of deepfakes, detailing how generative deep learning algorithms enable realistic fake media and the methods used to identify them, without presenting new experimental results or numbers.
Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these `deepfakes' have advanced significantly. In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas which require further research and attention.