# On the Vulnerability of Capsule Networks to Adversarial Attacks

arXiv: https://arxiv.org/abs/1906.03612 · HTML: https://mlsanity.com/paper/1906.03612/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks](https://mlsanity.com/paper/2212.01806/) (2022)
- [Effective and Efficient Vote Attack on Capsule Networks](https://mlsanity.com/paper/2102.10055/) (2021)
- [Capsule Network is Not More Robust than Convolutional Network](https://mlsanity.com/paper/2103.15459/) (2021)
- [Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption](https://mlsanity.com/paper/2301.01583/) (2023)
- [The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining](https://mlsanity.com/paper/2202.08185/) (2022)
- [Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks](https://mlsanity.com/paper/2208.10279/) (2022)
- [Security Analysis of Capsule Network Inference using Horizontal Collaboration](https://mlsanity.com/paper/2109.11041/) (2021)
- [RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks](https://mlsanity.com/paper/2304.03973/) (2023)
- [Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training](https://mlsanity.com/paper/2202.13755/) (2022)
- [Effectiveness of the Recent Advances in Capsule Networks](https://mlsanity.com/paper/2210.05834/) (2022)

## Abstract (arXiv)

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.