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Updated: May 23, 2025

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Revisiting face forgery detection towards generalization.

Chunlei Peng1, Tao Chen1, Decheng Liu1

  • 1Xidian University, Xi'an 710071, Shaanxi, PR China.

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|March 11, 2025
PubMed
Summary
This summary is machine-generated.

This study provides the first comprehensive overview of generalizable face forgery detection methods. It addresses challenges like unknown forgeries and low-quality images to improve AI-generated face detection in real-world scenarios.

Keywords:
Cross dataset evaluationDamaged imageFace forgery detectionGeneralization abilityLiterature survey

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Forensics

Background:

  • Face forgery detection struggles with generative models that minimize artifacts.
  • Data compression and transmission can eliminate crucial forgery detection cues.
  • Existing methods lack generalization to novel, unknown, or low-quality forged images.

Purpose of the Study:

  • To present the first comprehensive overview of generalizable face forgery detection methods.
  • To analyze strategies for improving robustness against unknown forgeries and damaged images.
  • To guide future research towards real-world, unconstrained face forgery detection.

Main Methods:

  • Categorization of generalizable face forgery detection into robustness on novel forgeries and damaged images.
  • Discussion of generalization strategies: data augmentation, multi-source learning, fingerprint detection, feature enhancement, temporal analysis, and vision-language detection.
  • Summary of datasets and performance evaluation for state-of-the-art methods.

Main Results:

  • Identified key generalization strategies and their effectiveness.
  • Evaluated current methods' performance on robustness to novel and low-quality forgeries.
  • Highlighted limitations and future research directions for generalizable face forgery detection.

Conclusions:

  • Generalizable face forgery detection is crucial for real-world applications.
  • Further research is needed in dataset creation, cue extraction, identity features, detector security, large models, and test-time adaptation.
  • This overview aims to advance face forgery detection towards practical, unconstrained conditions.