The Creation and Detection of Deepfakes

Yisroel Mirsky, Wenke Lee

Research output: Contribution to journalReview articlepeer-review

181 Scopus citations


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 article, we explore the creation and detection of deepfakes and provide an in-depth view as to 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 that require further research and attention.

Original languageEnglish
Article number7
JournalACM Computing Surveys
Issue number1
StatePublished - 1 Apr 2021


  • Deepfake
  • deep fake
  • face swap
  • generative AI
  • impersonation
  • reenactment
  • replacement
  • social engineering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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