mlsanityData to 14 Oct 2026

Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers

arXiv 2506.10888 ↗First posted 12 Jun 2025cs.LGcited by 0

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Abstract (arXiv)

Finite mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, existing attacks have been shown to not suit this kind of classifier. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of attacks based on a geometrical analysis of the problem (effectiveness and maximality). We then show that existing attacks do not meet both of these properties. Finally, we introduce a new attack called em lattice climber attack with theoretical guarantees in the binary linear setting, and demonstrate its performance by conducting experiments on synthetic and real datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 12 Jun 2025.

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21 of 27 references.

References · 21

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  4. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  5. 2020RobustBench: a standardized adversarial robustness benchmarkcited by 661
  6. 2020On Adaptive Attacks to Adversarial Example Defensescited by 555
  7. 2017Mitigating Adversarial Effects Through Randomizationcited by 383
  8. 2019Improving Adversarial Robustness via Promoting Ensemble Diversitycited by 213
  9. 2018Stochastic Activation Pruning for Robust Adversarial Defensecited by 171
  10. 2018Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attackcited by 125
  11. 2018Adversarial examples from computational constraintscited by 80
  12. 2020DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensemblescited by 73
9 more
  1. 2019Improving Adversarial Robustness of Ensembles with Diversity Trainingcited by 69
  2. 2020Randomization matters. How to defend against strong adversarial attackscited by 37
  3. 2021Mixed Nash Equilibria in the Adversarial Examples Gamecited by 22
  4. 2022Demystifying the Adversarial Robustness of Random Transformation Defensescited by 14
  5. 2021Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustnesscited by 11
  6. 2022Building Robust Ensembles via Margin Boostingcited by 8
  7. 2019Robust Attacks against Multiple Classifierscited by 7
  8. 2022Adversarial Vulnerability of Randomized Ensemblescited by 5
  9. 2023On the Robustness of Randomized Ensembles to Adversarial Perturbationscited by 3

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