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Bridged adversarial training.

Hoki Kim1, Woojin Lee2, Sungyoon Lee3

  • 1Institute of Engineering Research, Seoul National University, Gwanak-gu 08826, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|September 4, 2023
PubMed
Summary
This summary is machine-generated.

Adversarial training can yield deep neural networks with similar robustness but different internal characteristics. Bridged adversarial training improves robustness, particularly for large perturbations, by addressing the trade-off between margin and smoothness.

Keywords:
Adversarial defenseAdversarial robustnessAdversarial trainingNeural networks

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Area of Science:

  • Deep learning
  • Machine learning security
  • Artificial intelligence

Background:

  • Adversarial robustness is crucial for deep neural networks.
  • Current adversarial training methods may obscure underlying model characteristics like margin and smoothness.
  • These differing characteristics can impact model performance and reliability.

Purpose of the Study:

  • To investigate the impact of regularizers on adversarial training.
  • To identify and mitigate the negative effects of smoothness regularization on margin maximization.
  • To propose a novel method for enhancing adversarial robustness.

Main Methods:

  • Analysis of margin and smoothness characteristics in adversarially trained models.
  • Investigation of various regularizers' effects.
  • Development of bridged adversarial training (BAT).

Main Results:

  • Adversarially trained models can exhibit diverse margin and smoothness properties despite similar robustness.
  • Smoothness regularization can negatively impact margin maximization.
  • Bridged adversarial training demonstrates stable and improved robustness, especially against large perturbations.

Conclusions:

  • Bridged adversarial training offers a more stable and effective approach to enhancing adversarial robustness.
  • The method successfully bridges the gap between clean and adversarial examples.
  • This approach is particularly beneficial for models facing significant adversarial perturbations.