Towards Better Certified Segmentation via Diffusion Models

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1185-1195
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event39th Conference on Uncertainty in Artificial Intelligence, UAI 2023 - Pittsburgh, United States
Duration: Jul 31 2023Aug 4 2023

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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