# Robustness to Adversarial Examples through an Ensemble of Specialists

arXiv: https://arxiv.org/abs/1702.06856 · HTML: https://mlsanity.com/paper/1702.06856/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 21 of 21)

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- [Uncertainty-Aware Deep Video Compression with Ensembles](https://mlsanity.com/paper/2403.19158/) (2024)
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- [Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO](https://mlsanity.com/paper/2206.06761/) (2022)
- [Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches](https://mlsanity.com/paper/2111.12631/) (2021)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
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- [Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes](https://mlsanity.com/paper/2303.02322/) (2023)
- [CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications](https://mlsanity.com/paper/2401.11126/) (2024)
- [Code Integrity Attestation for PLCs using Black Box Neural Network Predictions](https://mlsanity.com/paper/2106.07851/) (2021)
- [Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning](https://mlsanity.com/paper/2107.02052/) (2021)
- [Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks](https://mlsanity.com/paper/2108.10217/) (2021)
- [Defending Adversarial Examples by Negative Correlation Ensemble](https://mlsanity.com/paper/2206.10334/) (2022)
- [A Perturbation Resistant Transformation and Classification System for Deep Neural Networks](https://mlsanity.com/paper/2208.11839/) (2022)
- [Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training](https://mlsanity.com/paper/2312.00105/) (2023)

## Abstract (arXiv)

We are proposing to use an ensemble of diverse specialists, where speciality is defined according to the confusion matrix. Indeed, we observed that for adversarial instances originating from a given class, labeling tend to be done into a small subset of (incorrect) classes. Therefore, we argue that an ensemble of specialists should be better able to identify and reject fooling instances, with a high entropy (i.e., disagreement) over the decisions in the presence of adversaries. Experimental results obtained confirm that interpretation, opening a way to make the system more robust to adversarial examples through a rejection mechanism, rather than trying to classify them properly at any cost.