On the Vulnerability of Capsule Networks to Adversarial Attacks

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.

5 pages

References in corpus (3)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Capsule Network Performance on Complex Data
• CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks

Cited by in corpus (2)

• Effective and Efficient Vote Attack on Capsule Networks
• Security Analysis of Capsule Network Inference using Horizontal Collaboration