Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Assessment framework for deepfake detection in real-world situations
 
research article

Assessment framework for deepfake detection in real-world situations

Lu, Yuhang  
•
Ebrahimi, Touradj  
2024
EURASIP Journal on Image and Video Processing

Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of four popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors.

  • Files
  • Details
  • Metrics
Type
research article
DOI
10.1186/s13640-024-00621-8
Author(s)
Lu, Yuhang  
Ebrahimi, Touradj  
Date Issued

2024

Published in
EURASIP Journal on Image and Video Processing
Volume

2024

Issue

1

Subjects

Assessment framework

•

Deepfake detection

•

Data augmentation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
GR-EB  
Available on Infoscience
February 14, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/203755
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés