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Deep neural networks have been playing an essential role in the task ofVisual Question Answering (VQA). Until recently, their accuracy has been themain focus of research. Now there is a trend toward assessing the robustness ofthese models against adversarial attacks by evaluating the accuracy of thesemodels under increasing levels of noisiness in the inputs of VQA models. InVQA, the attack can target the image and/or the proposed query question, dubbedmain question, and yet there is a lack of proper analysis of this aspect ofVQA. In this work, we propose a new method that uses semantically relatedquestions, dubbed basic questions, acting as noise to evaluate the robustnessof VQA models. We hypothesize that as the similarity of a basic question to themain question decreases, the level of noise increases. To generate a reasonablenoise level for a given main question, we rank a pool of basic questions basedon their similarity with this main question. We cast this ranking problem as aLASSO optimization problem. We also propose a novel robustness measure Rscoreand two large-scale basic question datasets in order to standardize robustnessanalysis of VQA models. The experimental results demonstrate that the proposedevaluation method is able to effectively analyze the robustness of VQA models.To foster the VQA research, we will publish our proposed datasets.

Assessing the Robustness of Visual Question Answering Models