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Zhiyuan Zhang1, Ruixuan Luo2, Xuancheng Ren1

  • 1MOE Key Laboratory of Computational Linguistics, School of EECS, Peking University, Beijing, China.

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|September 9, 2021
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Summary
This summary is machine-generated.

This study introduces parameter corruption for deep neural networks (DNNs) and proposes adversarial parameter defense to improve model robustness and accuracy against such vulnerabilities.

Keywords:
Adversarial parameter defenseParameter corruptionVulnerability of deep neural networks

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep neural networks (DNNs) are vulnerable to adversarial examples and parameter corruptions.
  • Understanding parameter corruption is crucial for enhancing model robustness and generalization.

Purpose of the Study:

  • To introduce and analyze parameter corruption in DNNs.
  • To propose defense mechanisms against parameter corruptions.
  • To enhance the robustness and accuracy of neural networks.

Main Methods:

  • Introduced the concept of parameter corruption.
  • Utilized loss change indicators to measure loss basin flatness and parameter robustness.
  • Developed a multi-step adversarial corruption algorithm.
  • Proposed an adversarial parameter defense algorithm to minimize risks from multiple corruptions.

Main Results:

  • The proposed adversarial parameter defense algorithm enhances neural network parameter robustness.
  • The defense algorithm also improves the overall accuracy of neural networks.
  • Experimental results validate the effectiveness of the proposed defense strategy.

Conclusions:

  • Parameter corruption is a significant vulnerability in DNNs.
  • Adversarial parameter defense offers a viable strategy to mitigate these vulnerabilities.
  • The proposed methods contribute to building more robust and reliable deep learning models.