mlsanityData to 14 Oct 2026

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

arXiv 2303.14961 ↗First posted 27 Mar 2023cs.LG cs.AI cs.CVcited by 5

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Abstract (arXiv)

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a ℓ2-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of ~ 13 % / 5% relative to previous approaches.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 41 of its 61 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

41 of 61 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  2. 2023The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothingcited by 8
  3. 2025RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samplescited by 7
  4. 2024Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddingscited by 6
  5. 2026Fortifying Time Series: DTW-Certified Robust Anomaly Detectioncited by 1