mlsanityData to 7 Oct 2026

On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks

arXiv 2210.02191 ↗First posted 3 Oct 2022cs.LG cs.AI cs.CVcited by 4

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Abstract (arXiv)

In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems. Targeting at the goal of estimating uncertainty, various deep neural network (DNN) based uncertainty estimation algorithms have been proposed. However, the robustness of the uncertainty returned by these algorithms has not been systematically explored. In this work, to raise the awareness of the research community on robust uncertainty estimation, we show that state-of-the-art uncertainty estimation algorithms could fail catastrophically under our proposed adversarial attack despite their impressive performance on uncertainty estimation. In particular, we aim at attacking the out-domain uncertainty estimation: under our attack, the uncertainty model would be fooled to make high-confident predictions for the out-domain data, which they originally would have rejected. Extensive experimental results on various benchmark image datasets show that the uncertainty estimated by state-of-the-art methods could be easily corrupted by our attack.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

17 of 32 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  7. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  8. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  9. 2018Adversarial Logit Pairingcited by 224
  10. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  11. 2018Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detectioncited by 82
  12. 2020Adversarial Robustness on In- and Out-Distribution Improves Explainabilitycited by 73
5 more
  1. 2019Towards neural networks that provably know when they don't knowcited by 69
  2. 2017Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networkscited by 48
  3. 2021Disrupting Deep Uncertainty Estimation Without Harming Accuracycited by 19
  4. 2021Provably Robust Detection of Out-of-distribution Data (almost) for freecited by 17
  5. 2021Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networkscited by 4

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.

4 papers.

  1. 2025Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgottencited by 3
  2. 2022QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptationcited by 2
  3. 2024Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networkscited by 2
  4. 2024On the Robustness of Adversarial Training Against Uncertainty Attackscited by 2