  Assessing the robustness of multimodal models against adversarial examples is
an important aspect for the safety of its users. We craft L0-norm perturbation
attacks on the preprocessed input images. We launch them in a black-box setup
against four multimodal models and two unimodal DNNs, considering both targeted
and untargeted misclassification. Our attacks target less than 0.04% of
perturbed image area and integrate different spatial positioning of perturbed
pixels: sparse positioning and pixels arranged in different contiguous shapes
(row, column, diagonal, and patch). To the best of our knowledge, we are the
first to assess the robustness of three state-of-the-art multimodal models
(ALIGN, AltCLIP, GroupViT) against different sparse and contiguous pixel
distribution perturbations. The obtained results indicate that unimodal DNNs
are more robust than multimodal models. Furthermore, models using CNN-based
Image Encoder are more vulnerable than models with ViT - for untargeted
attacks, we obtain a 99% success rate by perturbing less than 0.02% of the
image area.
