# Colored Noise Injection for Training Adversarially Robust Neural Networks

arXiv: https://arxiv.org/abs/2003.02188 · HTML: https://mlsanity.com/paper/2003.02188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

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

- [Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches](https://mlsanity.com/paper/2202.10320/) (2022)
- [Neural Variability Enhances Artificial Network Robustness](https://mlsanity.com/paper/2606.13801/) (2026)

## Abstract (arXiv)

Even though deep learning has shown unmatched performance on various tasks, neural networks have been shown to be vulnerable to small adversarial perturbations of the input that lead to significant performance degradation. In this work we extend the idea of adding white Gaussian noise to the network weights and activations during adversarial training (PNI) to the injection of colored noise for defense against common white-box and black-box attacks. We show that our approach outperforms PNI and various previous approaches in terms of adversarial accuracy on CIFAR-10 and CIFAR-100 datasets. In addition, we provide an extensive ablation study of the proposed method justifying the chosen configurations.