A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods

Gourav Gupta, Kiran Raja, Manish Gupta, Tony Jan, Scott Thompson Whiteside, Mukesh Prasad

Research output: Contribution to journalReview articlepeer-review

Abstract

Recent advances in Generative Artificial Intelligence (AI) have increased the possibility of generating hyper-realistic DeepFake videos or images to cause serious harm to vulnerable children, individuals, and society at large with misinformation. To overcome this serious problem, many researchers have attempted to detect DeepFakes using advanced machine learning techniques and advanced fusion techniques. This paper presents a detailed review of past and present DeepFake detection methods with a particular focus on media-modality fusion and machine learning. This paper also provides detailed information on available benchmark datasets in DeepFake detection research. This review paper addressed the 67 primary papers that were published between 2015 and 2023 in DeepFake detection, including 55 research papers in image and video DeepFake detection methodologies and 15 research papers on identifying and verifying speaker authentication. This paper offers lucrative information on DeepFake detection research and offers a unique review analysis of advanced machine learning and modality fusion that sets it apart from other review papers. This paper further offers informed guidelines for future work in DeepFake detection utilizing advanced state-of-the-art machine learning and information fusion models that should support further advancement in DeepFake detection for a sustainable and safer digital future.

Original languageEnglish
Article number95
JournalElectronics (Switzerland)
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 2024

Keywords

  • advanced machine learning in DeepFake detection
  • comprehensive review of DeepFake detection
  • DeepFake detection
  • modality fusion in DeepFake detection

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