# Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

**Authors:** Dan Hendrycks, Thomas Dietterich  
**arXiv:** [1903.12261](https://arxiv.org/abs/1903.12261)  
**Date:** 2019-03-28  
**Summary page:** https://www.summarizepaper.com/en/paper/1903.12261v1/

## Key points

- Authors Dan Hendrycks and Thomas Dietterich introduce benchmarks for assessing image classifier robustness
- ImageNet-C benchmark standardizes discussion on corruption robustness in image classification
- ImageNet-P dataset evaluates classifier's robustness against common perturbations
- Minimal differences in corruption robustness between AlexNet and ResNet classifiers
- Strategies explored to enhance both corruption and perturbation robustness in neural networks
- Comprehensive benchmarks aim to guide future research towards developing more effective neural networks

## Summary

In their paper titled "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations," authors Dan Hendrycks and Thomas Dietterich introduce rigorous benchmarks for assessing the robustness of image classifiers. The first benchmark, ImageNet-C, aims to standardize and expand the discussion on corruption robustness in image classification. This benchmark not only identifies which classifiers are more suitable for safety-critical applications but also sheds light on the performance of these classifiers under various common corruptions. Additionally, the authors propose a new dataset called ImageNet-P, which allows researchers to evaluate a classifier's robustness against common perturbations. Unlike previous studies that focused on worst-case adversarial perturbations, this benchmark assesses how well classifiers perform when faced with everyday distortions and perturbations. Surprisingly, the study reveals that there are minimal differences in corruption robustness between AlexNet and ResNet classifiers. Furthermore, the authors explore strategies to enhance both corruption and perturbation robustness in neural networks. They discover that even a previously bypassed adversarial defense mechanism can significantly improve a classifier's resilience to common perturbations. By providing these comprehensive benchmarks, the authors aim to guide future research towards developing neural networks that can generalize effectively across various real-world scenarios. Overall, this study contributes valuable insights into improving the robustness of image classifiers and highlights the importance of evaluating performance under common corruptions and perturbations for practical applications in computer vision.