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survey

Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

Published: 11 November 2024 Publication History

Abstract

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection.

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cover image ACM Computing Surveys
ACM Computing Surveys  Volume 57, Issue 3
March 2025
984 pages
EISSN:1557-7341
DOI:10.1145/3697147
  • Editors:
  • David Atienza,
  • Michela Milano
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New York, NY, United States

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Published: 11 November 2024
Online AM: 08 October 2024
Accepted: 02 October 2024
Revised: 10 July 2024
Received: 28 February 2023
Published in CSUR Volume 57, Issue 3

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  1. Deepfake detection
  2. reliability study
  3. forensic investigation
  4. confidence interval

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  • National Key R&D Program of China
  • Hunan Provincial Funds for Distinguished Young Scholars

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