# Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

arXiv: https://arxiv.org/abs/2303.02322 · HTML: https://mlsanity.com/paper/2303.02322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [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)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective](https://mlsanity.com/paper/2010.03316/) (2020)
- [Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong](https://mlsanity.com/paper/1706.04701/) (2017)
- [EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks](https://mlsanity.com/paper/2004.10162/) (2020)
- [Robustness to Adversarial Examples through an Ensemble of Specialists](https://mlsanity.com/paper/1702.06856/) (2017)
- [Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks](https://mlsanity.com/paper/1912.00181/) (2019)
- [Integer Programming-based Error-Correcting Output Code Design for Robust Classification](https://mlsanity.com/paper/2011.00144/) (2020)
- [Deep Convolutional Neural Network Ensembles using ECOC](https://mlsanity.com/paper/2009.02961/) (2020)

## Built on by (most cited first, 1 of 1)

- [Contrastive ECOC: Learning Output Codes for Adversarial Defense](https://mlsanity.com/paper/2508.10491/) (2025)

## Abstract (arXiv)

Neural network ensembles have been studied extensively in the context of adversarial robustness and most ensemble-based approaches remain vulnerable to adaptive attacks. In this paper, we investigate the robustness of Error-Correcting Output Codes (ECOC) ensembles through architectural improvements and ensemble diversity promotion. We perform a comprehensive robustness assessment against adaptive attacks and investigate the relationship between ensemble diversity and robustness. Our results demonstrate the benefits of ECOC ensembles for adversarial robustness compared to regular ensembles of convolutional neural networks (CNNs) and show why the robustness of previous implementations is limited. We also propose an adversarial training method specific to ECOC ensembles that allows to further improve robustness to adaptive attacks.