On the Vulnerability of Capsule Networks to Adversarial Attacks
arXiv:1906.03612v125 citations
Originality Synthesis-oriented
AI Analysis
This addresses the security of machine learning models for practitioners, but it is incremental as it refutes existing claims without introducing new methods.
The paper tackles the problem of evaluating the robustness of capsule networks to adversarial attacks, finding that they can be fooled as easily as convolutional neural networks, contrary to prior suggestions of greater robustness.
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.