  The robustness of image segmentation has been an important research topic in
the past few years as segmentation models have reached production-level
accuracy. However, like classification models, segmentation models can be
vulnerable to adversarial perturbations, which hinders their use in
critical-decision systems like healthcare or autonomous driving. Recently,
randomized smoothing has been proposed to certify segmentation predictions by
adding Gaussian noise to the input to obtain theoretical guarantees. However,
this method exhibits a trade-off between the amount of added noise and the
level of certification achieved. In this paper, we address the problem of
certifying segmentation prediction using a combination of randomized smoothing
and diffusion models. Our experiments show that combining randomized smoothing
and diffusion models significantly improves certified robustness, with results
indicating a mean improvement of 21 points in accuracy compared to previous
state-of-the-art methods on Pascal-Context and Cityscapes public datasets. Our
method is independent of the selected segmentation model and does not need any
additional specialized training procedure.
