# One Neuron to Fool Them All

arXiv: https://arxiv.org/abs/2003.09372 · HTML: https://mlsanity.com/paper/2003.09372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing](https://mlsanity.com/paper/2307.11011/) (2023)
- [Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence](https://mlsanity.com/paper/2112.13060/) (2021)

## Abstract (arXiv)

Despite vast research in adversarial examples, the root causes of model susceptibility are not well understood. Instead of looking at attack-specific robustness, we propose a notion that evaluates the sensitivity of individual neurons in terms of how robust the model's output is to direct perturbations of that neuron's output. Analyzing models from this perspective reveals distinctive characteristics of standard as well as adversarially-trained robust models, and leads to several curious results. In our experiments on CIFAR-10 and ImageNet, we find that attacks using a loss function that targets just a single sensitive neuron find adversarial examples nearly as effectively as ones that target the full model. We analyze the properties of these sensitive neurons to propose a regularization term that can help a model achieve robustness to a variety of different perturbation constraints while maintaining accuracy on natural data distributions. Code for all our experiments is available at https://github.com/iamgroot42/sauron .