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Publication Detail
Learning Universal Adversarial Perturbations with Generative Models
  • Publication Type:
    Conference
  • Authors:
    Hayes J, Danezis G
  • Publisher:
    IEEE
  • Publication date:
    06/08/2018
  • Pagination:
    43, 49
  • Published proceedings:
    Proceedings of the Security and Privacy Workshops (SPW) 2018 IEEE
  • ISBN-13:
    978-1-5386-8276-0
  • Status:
    Published
  • Name of conference:
    Security and Privacy Workshops (SPW)
  • Conference place:
    San Francisco (CA), USA
  • Conference start date:
    24/05/2018
  • Conference finish date:
    24/05/2018
  • Keywords:
    Perturbation methods, Measurement, Training, Error analysis, Atmospheric modeling, Security, Machine learning
Abstract
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and classifier, there exists so called universal adversarial perturbations, a single perturbation that causes a misclassification when applied to any input. In this work, we introduce universal adversarial networks, a generative network that is capable of fooling a target classifier when it's generated output is added to a clean sample from a dataset. We show that this technique improves on known universal adversarial attacks.
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