Hayes, J;
Danezis, G;
(2018)
Learning Universal Adversarial Perturbations with Generative Models.
In:
Proceedings of the Security and Privacy Workshops (SPW) 2018 IEEE.
(pp. pp. 43-49).
IEEE: San Francisco (CA), USA.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Universal Adversarial Perturbations with Generative Models |
| Event: | Security and Privacy Workshops (SPW) |
| Location: | San Francisco (CA), USA |
| Dates: | 24th May 2018 |
| ISBN-13: | 978-1-5386-8276-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SPW.2018.00015 |
| Publisher version: | https://doi.org/10.1109/SPW.2018.00015 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Perturbation methods, Measurement, Training, Error analysis, Atmospheric modeling, Security, Machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10059181 |
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