Understanding and Measuring Robustness of Multimodal Learning. (arXiv:2112.12792v1 [cs.LG])

The modern digital world is increasingly becoming multimodal. Although
multimodal learning has recently revolutionized the state-of-the-art
performance in multimodal tasks, relatively little is known about the
robustness of multimodal learning in an adversarial setting. In this paper, we
introduce a comprehensive measurement of the adversarial robustness of
multimodal learning by focusing on the fusion of input modalities in multimodal
models, via a framework called MUROAN (MUltimodal RObustness ANalyzer). We
first present a unified view of multimodal models in MUROAN and identify the
fusion mechanism of multimodal models as a key vulnerability. We then introduce
a new type of multimodal adversarial attacks called decoupling attack in MUROAN
that aims to compromise multimodal models by decoupling their fused modalities.
We leverage the decoupling attack of MUROAN to measure several state-of-the-art
multimodal models and find that the multimodal fusion mechanism in all these
models is vulnerable to decoupling attacks. We especially demonstrate that, in
the worst case, the decoupling attack of MUROAN achieves an attack success rate
of 100% by decoupling just 1.16% of the input space. Finally, we show that
traditional adversarial training is insufficient to improve the robustness of
multimodal models with respect to decoupling attacks. We hope our findings
encourage researchers to pursue improving the robustness of multimodal
learning.

Source: https://arxiv.org/abs/2112.12792

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