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Updated: Jul 30, 2025

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Face Database Protection via Beautification with Chaotic Systems.

Tao Wang1, Yushu Zhang1, Ruoyu Zhao1

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Entropy (Basel, Switzerland)
|May 16, 2023
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Summary

This study introduces a novel face privacy protection method using beautification and chaotic systems. It effectively defends against unauthorized facial recognition while maintaining visual quality and enhancing data security.

Keywords:
adversarialbeautificationchaotic systemsdatabase protection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Facial recognition systems pose privacy risks to sensitive face databases.
  • Existing adversarial perturbation methods cause visual distortions, limiting privacy protection.
  • Social media trends increase demand for face beautification, impacting privacy concerns.

Purpose of the Study:

  • To develop a novel face database protection scheme using beautification and chaotic systems.
  • To enhance visual perception of adversarial faces while protecting privacy.
  • To mitigate privacy leakage risks from unauthorized facial recognition.

Main Methods:

  • Constructing adversarial faces with beautification to confuse face matchers.
  • Employing a federated training approach with a face matcher and beautification discriminator against a generator.
  • Utilizing chaotic systems to randomize adversarial face order in the database.

Main Results:

  • The proposed scheme effectively defends against unauthorized facial recognition attacks.
  • Beautification-induced pixel changes mask adversarial perturbations, preserving visual quality.
  • Chaotic systems further reduce privacy leakage risks by disrupting data order.

Conclusions:

  • The novel scheme offers robust privacy protection for face databases.
  • It balances security with aesthetic appeal, addressing user preferences.
  • This approach represents a significant advancement in secure facial data management.