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Related Experiment Video

Updated: Aug 9, 2025

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Towards a Controllable and Reversible Privacy Protection System for Facial Images through Enhanced Multi-Factor

Yi-Lun Pan1,2, Jun-Cheng Chen3, Ja-Ling Wu1,4

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei 116, Taiwan.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel privacy protection system that uses a Multi-factor Modifier (MfM) to anonymize and de-anonymize face images controllably. The system offers strong security with multi-factor identification, ensuring data privacy for personal images.

Keywords:
anonymize (de-identification)de-anonymize (re-identification)generative adversarial networks (GAN)mutual information

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Mobile devices capture sensitive personal images, necessitating robust privacy protection.
  • Existing methods struggle with controllable and reversible anonymization of facial data.

Purpose of the Study:

  • To develop a controllable and reversible privacy protection system for facial images.
  • To enhance security through multi-factor identification for anonymization and de-anonymization.

Main Methods:

  • A modified conditional Generative Adversarial Network (GAN) framework, termed Multi-factor Modifier (MfM).
  • Incorporation of physically meaningful information-theory-based loss functions.
  • Utilizing multi-factor features (e.g., gender, hair color, passwords) for identification.

Main Results:

  • MfM successfully anonymizes and de-anonymizes face images with high fidelity and diversity.
  • Achieved superior de-identification effects compared to state-of-the-art methods (LPIPS: 0.35, FID: 28, SSIM: 0.95).
  • Demonstrated effective re-identification capabilities, enhancing real-world applicability.

Conclusions:

  • The MfM system provides a secure, controllable, and reversible solution for facial image privacy protection.
  • The proposed information-theory-based approach and multi-factor identification significantly improve anonymization and de-anonymization performance.
  • MfM offers practical advantages for safeguarding personal visual data in the digital age.