# Sensitivity of Deep Convolutional Networks to Gabor Noise

arXiv: https://arxiv.org/abs/1906.03455 · HTML: https://mlsanity.com/paper/1906.03455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Jacobian Regularization for Mitigating Universal Adversarial Perturbations](https://mlsanity.com/paper/2104.10459/) (2021)
- [Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks](https://mlsanity.com/paper/2204.08726/) (2022)
- [On Procedural Adversarial Noise Attack And Defense](https://mlsanity.com/paper/2108.04409/) (2021)

## Abstract (arXiv)

Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually similar procedural noise patterns also act as UAPs. In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns. This behaviour, its causes, and implications deserve further in-depth study.