# Understanding and Measuring Robustness of Multimodal Learning

**Authors:** Nishant Vishwamitra, Hongxin Hu, Ziming Zhao, Long Cheng, Feng Luo  
**arXiv:** [2112.12792](https://arxiv.org/abs/2112.12792)  
**Date:** 2021-12-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.12792v2/

## Key points

- The paper focuses on multimodal learning and introduces MUROAN, a framework to measure adversarial robustness in multimodal models.
- The authors identify the fusion mechanism as a vulnerability and introduce the decoupling attack to compromise multimodal models by separating fused modalities.
- Decoupling attacks were successful in compromising state-of-the-art multimodal models with minimal manipulation of just 1.16% of the input space.
- Traditional adversarial training methods are insufficient in improving robustness against decoupling attacks.
- Previous works on unimodal adversarial text highlight vulnerabilities in NLP models, emphasizing the need for comprehensive model defense across various modalities.
- The paper underscores the critical importance of enhancing robustness in multimodal learning systems to ensure effectiveness and security in real-world applications.

## Summary

The paper "Understanding and Measuring Robustness of Multimodal Learning" by Nishant Vishwamitra, Hongxin Hu, Ziming Zhao, Long Cheng, and Feng Luo delves into the increasingly prevalent realm of multimodal learning in the modern digital world. The authors introduce MUROAN (MUltimodal RObustness ANalyzer), a framework designed to comprehensively measure the adversarial robustness of multimodal learning by focusing on the fusion of input modalities within multimodal models. Through MUROAN, they identify the fusion mechanism as a key vulnerability in these models and introduce a new type of adversarial attack called decoupling attack. This attack aims to compromise multimodal models by separating their fused modalities. By leveraging the decoupling attack within MUROAN, the authors assess several state-of-the-art multimodal models and discover that the fusion mechanism in all these models is susceptible to decoupling attacks. They demonstrate that even with minimal manipulation of just 1.16% of the input space, the decoupling attack can achieve an alarming 100% success rate in compromising these models. Furthermore, traditional adversarial training methods are found to be insufficient in improving the robustness of multimodal models against decoupling attacks. The findings presented in this paper highlight the critical need for researchers to focus on enhancing the robustness of multimodal learning systems to ensure their effectiveness and security in real-world applications. Additionally, previous works on unimodal adversarial text have explored different strategies such as character-level perturbations and word replacement techniques to compromise NLP models. These studies provide valuable insights into vulnerabilities present within unimodal systems and underscore the importance of addressing adversarial challenges across various modalities for comprehensive model defense. In conclusion, this paper contributes significantly to advancing our understanding of robustness issues in multimodal learning and emphasizes the urgency for further research efforts aimed at fortifying these systems against adversarial attacks across diverse modalities.