# Defense Through Diverse Directions

arXiv: https://arxiv.org/abs/2003.10602 · HTML: https://mlsanity.com/paper/2003.10602/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

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

- [Demystifying the Adversarial Robustness of Random Transformation Defenses](https://mlsanity.com/paper/2207.03574/) (2022)
- [How Sampling Impacts the Robustness of Stochastic Neural Networks](https://mlsanity.com/paper/2204.10839/) (2022)

## Abstract (arXiv)

In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training. Unlike previous efforts in this direction, we do not rely solely on the stochasticity of network weights by minimizing the divergence between the learned parameter distribution and a prior. Instead, we additionally require that the model maintain some expected uncertainty with respect to all input covariates. We demonstrate that by encouraging the network to distribute evenly across inputs, the network becomes less susceptible to localized, brittle features which imparts a natural robustness to targeted perturbations. We show empirical robustness on several benchmark datasets.