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Decoupling visual and identity features for adversarial palm-vein image attack.

Jiacheng Yang1, Wai Keung Wong2, Lunke Fei1

  • 1The School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network to create adversarial palm-vein images that look real but have altered identities. This research addresses vulnerabilities in palm-vein recognition systems against sophisticated image attacks.

Keywords:
Adversarial attackBlack-boxGenerator-orientedPalm-vein imagesWhite-box

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Area of Science:

  • Biometrics
  • Computer Vision
  • Cybersecurity

Background:

  • Palm-vein biometrics offer high security against theft and forgery.
  • Existing palm-vein recognition systems are vulnerable to adversarial image attacks.
  • No prior studies have specifically focused on adversarial attacks against palm-vein images.

Purpose of the Study:

  • To propose a novel adversarial palm-vein image attack network.
  • To generate adversarial palm-vein images that preserve visual similarity while altering identity.
  • To enhance the security robustness of palm-vein recognition systems.

Main Methods:

  • Developed an adversarial palm-vein image attack network.
  • Employed multi-scale high-dimensional shallow representation mapping.
  • Utilized attention-based dual-path feature learning modules.
  • Designed visual-consistency and identity-aware loss functions for feature decoupling.

Main Results:

  • Generated adversarial palm-vein images with high visual similarity to original samples.
  • Successfully altered palm-identity information in generated images.
  • Demonstrated effectiveness through extensive white-box and black-box attack experiments on three databases.

Conclusions:

  • The proposed network effectively generates aggressive adversarial palm-vein samples.
  • The method preserves visual characteristics while removing identity information.
  • This work highlights critical security vulnerabilities in current palm-vein recognition technologies.