mlsanityData to 7 Oct 2026

Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness

arXiv 2406.17830 ↗First posted 25 Jun 2024stat.ML cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

Randomized smoothing is a popular certified defense against adversarial attacks. In its essence, we need to solve a problem of statistical estimation which is usually very time-consuming since we need to perform numerous (usually 105) forward passes of the classifier for every point to be certified. In this paper, we review the statistical estimation problems for randomized smoothing to find out if the computational burden is necessary. In particular, we consider the (standard) task of adversarial robustness where we need to decide if a point is robust at a certain radius or not using as few samples as possible while maintaining statistical guarantees. We present estimation procedures employing confidence sequences enjoying the same statistical guarantees as the standard methods, with the optimal sample complexities for the estimation task and empirically demonstrate their good performance. Additionally, we provide a randomized version of Clopper-Pearson confidence intervals resulting in strictly stronger certificates.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 35 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

21 of 35 references.

References · 21

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  5. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  6. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  7. 2019Online Learning: A Modern Introduction Using Convex Optimizationcited by 407
  8. 2019Provably Robust Deep Learning via Adversarially Trained Smoothed Classifierscited by 328
  9. 2020Estimating means of bounded random variables by bettingcited by 164
  10. 2020Randomized Smoothing of All Shapes and Sizescited by 146
  11. 2019Mean estimation and regression under heavy-tailed distributions--a surveycited by 133
  12. 2020(De)Randomized Smoothing for Certifiable Defense against Patch Attackscited by 97
9 more
  1. 2019Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifierscited by 65
  2. 2020Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and Morecited by 56
  3. 2021Boosting Randomized Smoothing with Variance Reduced Classifierscited by 47
  4. 2021Tight Concentrations and Confidence Sequences from the Regret of Universal Portfoliocited by 42
  5. 2021Improved, Deterministic Smoothing for L_1 Certified Robustnesscited by 39
  6. 2023Incremental Randomized Smoothing Certificationcited by 12
  7. 2021Input-Specific Robustness Certification for Randomized Smoothingcited by 11
  8. 2022On Confidence Sequences for Bounded Random Processes via Universal Gambling Strategiescited by 8
  9. 2024Gambling-Based Confidence Sequences for Bounded Random Vectorscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Bridging the Theoretical Gap in Randomized Smoothingcited by 2
  2. 2025STaR-Bets: Sequential Target-Recalculating Bets for Tighter Confidence Intervalscited by 1
  3. 2025Randomized Smoothing Meets Vision-Language Modelscited by 1
  4. 2026Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?cited by 0
  5. 2026Laplace-Bridged Randomized Smoothing for Fast Certified Robustnesscited by 0
  6. 2026RRISE: Robust Radius Inference via a Surrogate Estimatorcited by 0
  7. 2026Halt Fast! Early Stopping for Certified Robustnesscited by 0
  8. 2026What Was That Again? Certified Robustness for Automatic Speech Recognitioncited by 0