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Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks.

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  • 1Department for MicroData Analytics, Dalarna University, 791 88 Falun, Sweden.

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Summary

Dropout regularization enhances neural network resilience to adversarial attacks, but optimal results depend on specific dropout probabilities and minimizing functional smearing, where neurons serve multiple functions.

Keywords:
adversarial attacksartificial neural networksdropoutfast gradient sign methodinformation relayinformation smearedness

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep learning models excel at tasks but are prone to overfitting and adversarial attacks.
  • Dropout regularization is a known technique to improve model generalization and robustness.

Purpose of the Study:

  • To investigate the effect of dropout regularization on neural network robustness against adversarial attacks.
  • To analyze the relationship between dropout regularization, functional smearing, and adversarial resilience.

Main Methods:

  • Examined the impact of varying dropout probabilities on neural network performance.
  • Quantified functional smearing, defined as neurons involved in multiple functions.
  • Assessed network resistance to adversarial attacks under different dropout conditions.

Main Results:

  • Dropout regularization improves resistance to adversarial attacks within a specific probability range.
  • Dropout significantly increases functional smearing across various dropout rates.
  • Networks with lower functional smearing demonstrate greater adversarial resilience.

Conclusions:

  • Dropout regularization enhances robustness to adversarial attacks but is sensitive to dropout probability.
  • Reducing functional smearing is crucial for improving adversarial resilience, even with dropout.
  • Balancing dropout for generalization and minimizing functional smearing is key for robust deep learning models.