Adversarial Attacks.pdf

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Preview of Adversarial Attacks
🔗 Source: usenix.org
📊 Size: 4.34 MB
📄 Pages: 21 pages
⬇️ Downloads: 293

Summary

Adversarial examples can mislead machine learning models, with 88% of a "Tabby Cat" image and 99% of a "Guacamole" image being misclassified. Many defenses have been proposed, but evaluating them is hard; re-evaluation of 13 defenses presented at ICLR, ICML, and NeurIPS from 2018 to 2020 showed that all could be circumvented, reducing accuracy to baseline levels.

Description

Adversarial examples can mislead machine learning models, with 88% of a "Tabby Cat" image and 99% of a "Guacamole" image being misclassified.

Technical Information

  • File Format: PDF
  • File Size: 4.34 MB
  • Pages: 21
  • Language: EN
  • Total Downloads: 293
  • Last Updated: 4 hours ago

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