# Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers

arXiv: https://arxiv.org/abs/2506.10888 · HTML: https://mlsanity.com/paper/2506.10888/

## 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.

## Builds on (most cited first, 21 of 21)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [Stochastic Activation Pruning for Robust Adversarial Defense](https://mlsanity.com/paper/1803.01442/) (2018)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [Adversarial examples from computational constraints](https://mlsanity.com/paper/1805.10204/) (2018)
- [DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles](https://mlsanity.com/paper/2009.14720/) (2020)
- [Improving Adversarial Robustness of Ensembles with Diversity Training](https://mlsanity.com/paper/1901.09981/) (2019)
- [Randomization matters. How to defend against strong adversarial attacks](https://mlsanity.com/paper/2002.11565/) (2020)
- [Mixed Nash Equilibria in the Adversarial Examples Game](https://mlsanity.com/paper/2102.06905/) (2021)
- [Demystifying the Adversarial Robustness of Random Transformation Defenses](https://mlsanity.com/paper/2207.03574/) (2022)
- [Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness](https://mlsanity.com/paper/2112.02671/) (2021)
- [Building Robust Ensembles via Margin Boosting](https://mlsanity.com/paper/2206.03362/) (2022)
- [Robust Attacks against Multiple Classifiers](https://mlsanity.com/paper/1906.02816/) (2019)
- [Adversarial Vulnerability of Randomized Ensembles](https://mlsanity.com/paper/2206.06737/) (2022)
- [On the Robustness of Randomized Ensembles to Adversarial Perturbations](https://mlsanity.com/paper/2302.01375/) (2023)

## 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.