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Vulnerability of classifiers to evolutionary generated adversarial examples.

Petra Vidnerová1, Roman Neruda1

  • 1The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 271/2, 182 07 Prague 8, Czechia.

Neural Networks : the Official Journal of the International Neural Network Society
|May 4, 2020
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Summary

Machine learning models are vulnerable to adversarial examples, even without model access. Local computational units, like Gaussian kernels, show less vulnerability, improving model robustness.

Keywords:
Adversarial examplesGenetic algorithmsKernel methodsNeural networksSupervised learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning models face security risks from adversarial examples.
  • Understanding model vulnerability is crucial for developing robust AI systems.

Purpose of the Study:

  • To investigate the vulnerability of diverse machine learning models to adversarial examples.
  • To propose a black-box attack method for generating adversarial examples.

Main Methods:

  • An evolutionary algorithm was developed to generate adversarial examples.
  • The algorithm operates in a black-box setting, requiring only model queries.
  • Various machine learning models, including deep and shallow neural networks, were tested.

Main Results:

  • Adversarial vulnerability is widespread across different machine learning architectures, not limited to deep networks.
  • The type of computational units significantly impacts vulnerability.
  • Local units, such as Gaussian kernels, demonstrated reduced susceptibility to adversarial attacks.

Conclusions:

  • Machine learning models exhibit broad vulnerability to adversarial examples.
  • Architectural choices, specifically the use of local computational units, can enhance model robustness against such attacks.