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Related Experiment Video

Updated: Jul 26, 2025

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Face morphing attack detection based on high-frequency features and progressive enhancement learning.

Cheng-Kun Jia1, Yong-Chao Liu1, Ya-Ling Chen1

  • 1School of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hengyang, China.

Frontiers in Neurorobotics
|June 21, 2023
PubMed
Summary

This study introduces a novel face morphing detection method using high-frequency features and progressive enhancement learning. The approach effectively captures subtle image details, outperforming existing technologies in detecting sophisticated morphing attacks.

Keywords:
face morphing attackshigh-frequency featuresinteractive-enhancement modulemachine learningprogressive enhancement learningself-enhancement module

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Area of Science:

  • Computer Vision
  • Digital Image Forensics

Background:

  • Face morphing attacks are increasingly sophisticated, posing challenges for current detection methods.
  • Existing techniques struggle to capture fine-grained texture and detail alterations inherent in morphing.

Purpose of the Study:

  • To develop an advanced face morphing detection method.
  • To overcome limitations of existing methods in capturing subtle texture and detail changes.

Main Methods:

  • Extraction of high-frequency information from RGB image channels.
  • A progressive enhancement learning framework fusing high-frequency and RGB information.
  • Utilizing self-enhancement and interactive-enhancement modules to capture morphing traces.

Main Results:

  • The proposed method demonstrated excellent performance in detecting face morphing attacks.
  • Experimental results on a standard database confirmed its superiority over nine classical technologies.

Conclusions:

  • The developed method effectively detects complex face morphing attacks.
  • High-frequency features combined with progressive enhancement learning offer a robust solution for digital image forensics.