mlsanityData to 7 Oct 2026

Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations

arXiv 2106.14432 ↗First posted 28 Jun 2021cs.LGcited by 6

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Abstract (arXiv)

Currently the most popular method of providing robustness certificates is randomized smoothing where an input is smoothed via some probability distribution. We propose a novel approach to randomized smoothing over multiplicative parameters. Using this method we construct certifiably robust classifiers with respect to a gamma correction perturbation and compare the result with classifiers obtained via other smoothing distributions (Gaussian, Laplace, uniform). The experiments show that asymmetrical Rayleigh distribution allows to obtain better certificates for some values of perturbation parameters. To the best of our knowledge it is the first work concerning certified robustness against the multiplicative gamma correction transformation and the first to study effects of asymmetrical distributions in randomized smoothing.

Key facts. Cited by 6 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 28 Jun 2021.

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. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
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  8. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
  9. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  10. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  11. 2019Provably Robust Deep Learning via Adversarially Trained Smoothed Classifierscited by 328
  12. 2018Adversarial Risk and the Dangers of Evaluating Against Weak Attackscited by 221
9 more
  1. 2018Certified Adversarial Robustness with Additive Noisecited by 178
  2. 2018Semidefinite relaxations for certifying robustness to adversarial examplescited by 178
  3. 2017Towards Robust Neural Networks via Random Self-ensemblecited by 158
  4. 2017Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classificationcited by 56
  5. 2020Curse of Dimensionality on Randomized Smoothing for Certifiable Robustnesscited by 56
  6. 2020Certified Defense to Image Transformations via Randomized Smoothingcited by 51
  7. 2020TSS: Transformation-Specific Smoothing for Robustness Certificationcited by 49
  8. 2020Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Frameworkcited by 45
  9. 2020Extensions and limitations of randomized smoothing for robustness guaranteescited by 15

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 3.

6 papers.

  1. 2022Backdoor Cleansing with Unlabeled Datacited by 17
  2. 2022Invariance-Aware Randomized Smoothing Certificatescited by 7
  3. 2024Certification of Speaker Recognition Models to Additive Perturbationscited by 6
  4. 2024KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Modelscited by 3
  5. 2023General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothingcited by 0
  6. 2024Certified Adversarial Robustness via Partition-based Randomized Smoothingcited by 0