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arXiv:1906.03612cs.LG

On the Vulnerability of Capsule Networks to Adversarial Attacks

Felix Michels, Tobias Uelwer, Eric Upschulte, Stefan Harmeling

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Abstract

This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks. Recent work suggests that these architectures are more robust towards adversarial attacks than other neural networks. However, our experiments show that capsule networks can be fooled as easily as convolutional neural networks.

arXiv comments
5 pages
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.CRCryptography and Security stat.MLMachine Learning (Statistics)

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    • >Finally, not all SOTA methods are susceptible to adversarial attacks, eg capsule networks. They appear to be susceptible: https://arxiv.org/pdf/1906.03612.pdf

      throw99901View on HNParent context

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