# Biologically Inspired Mechanisms for Adversarial Robustness

arXiv: https://arxiv.org/abs/2006.16427 · HTML: https://mlsanity.com/paper/2006.16427/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 29 of 29)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [FoveaTer: Foveated Transformer for Image Classification](https://mlsanity.com/paper/2105.14173/) (2021)
- [PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution](https://mlsanity.com/paper/2403.07589/) (2024)
- [Peripheral Vision Transformer](https://mlsanity.com/paper/2206.06801/) (2022)
- [Adversarial Visual Robustness by Causal Intervention](https://mlsanity.com/paper/2106.09534/) (2021)
- [On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation](https://mlsanity.com/paper/2112.07173/) (2021)
- [A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains](https://mlsanity.com/paper/2310.13849/) (2023)
- [Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks](https://mlsanity.com/paper/2202.00838/) (2022)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention](https://mlsanity.com/paper/2204.09093/) (2022)
- [Training on Foveated Images Improves Robustness to Adversarial Attacks](https://mlsanity.com/paper/2308.00854/) (2023)
- [Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises](https://mlsanity.com/paper/2206.07282/) (2022)
- [LSFSL: Leveraging Shape Information in Few-shot Learning](https://mlsanity.com/paper/2304.06672/) (2023)
- [Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture](https://mlsanity.com/paper/2305.11275/) (2023)
- [Empirical Advocacy of Bio-inspired Models for Robust Image Recognition](https://mlsanity.com/paper/2205.09037/) (2022)
- [Perception Over Time: Temporal Dynamics for Robust Image Understanding](https://mlsanity.com/paper/2203.06254/) (2022)
- [The Effects of Image Distribution and Task on Adversarial Robustness](https://mlsanity.com/paper/2102.10534/) (2021)
- [Bio-inspired Robustness: A Review](https://mlsanity.com/paper/2103.09265/) (2021)
- [On Inherent Adversarial Robustness of Active Vision Systems](https://mlsanity.com/paper/2404.00185/) (2024)
- [Global-Local Processing in Convolutional Neural Networks](https://mlsanity.com/paper/2306.08336/) (2023)
- [Fixed Inter-Neuron Covariability Induces Adversarial Robustness](https://mlsanity.com/paper/2308.03956/) (2023)
- [When do Convolutional Neural Networks Stop Learning?](https://mlsanity.com/paper/2403.02473/) (2024)
- [Evaluating Adversarial Robustness in the Spatial Frequency Domain](https://mlsanity.com/paper/2405.06345/) (2024)
- [Sign-Symmetry Learning Rules are Robust Fine-Tuners](https://mlsanity.com/paper/2502.05925/) (2025)
- [SAFER-AiD: Saccade-Assisted Foveal-peripheral vision Enhanced Reconstruction for Adversarial Defense](https://mlsanity.com/paper/2510.08761/) (2025)
- [Temporal Slowness in Central Vision Drives Semantic Object Learning](https://mlsanity.com/paper/2602.04462/) (2026)
- [Retina gap junctions support the robust perception by warping neural representational geometries along the visual hierarchy](https://mlsanity.com/paper/2604.14200/) (2026)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)
- [FAVE: Foveated Adaptive Visual Encoding for Efficient Fine-Grained Visual Understanding](https://mlsanity.com/paper/2609.04392/) (2026)

## Abstract (arXiv)

A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated. The primate visual ventral stream seems to be robust to small perturbations in visual stimuli but the underlying mechanisms that give rise to this robust perception are not understood. In this work, we investigate the role of two biologically plausible mechanisms in adversarial robustness. We demonstrate that the non-uniform sampling performed by the primate retina and the presence of multiple receptive fields with a range of receptive field sizes at each eccentricity improve the robustness of neural networks to small adversarial perturbations. We verify that these two mechanisms do not suffer from gradient obfuscation and study their contribution to adversarial robustness through ablation studies.