# Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning Source: [Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:18:12.808Z Authors: Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim, Jung Uk Kim, Yong Man Ro Published: 2020-05-22T03:45:06.000Z ## Abstract The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive performance, relatively less attention has been devoted to the robustness of multimodal fusion models. In this paper, we investigated whether the current multimodal fusion model utilizes the complementary intelligence to defend against adversarial attacks. We applied gradient based white-box attacks such as FGSM and PGD on MFNet, which is a major multispectral (RGB, Thermal) fusion deep learning model for semantic segmentation. We verified that the multimodal fusion model optimized for better prediction is still vulnerable to adversarial attack, even if only one of the sensors is attacked. Thus, it is hard to say that existing multimodal data fusion models are fully utilizing complementary relationships between multiple modalities in terms of adversarial robustness. We believe that our observations open a new horizon for adversarial attack research on multimodal data fusion. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()