Impact of Colour Variation on Robustness of Deep Neural Networks

TL;DR

This paper investigates how variations in color space affect the accuracy of deep neural networks in image classification, revealing significant impacts and evaluating robustness strategies on a new color-distorted dataset.

Contribution

The study introduces a color-variation dataset for ImageNet and evaluates the impact of color distortions on DNN performance, also testing robustness techniques.

Findings

Color variation significantly reduces DNN accuracy.

Robust training techniques improve resilience to color distortions.

Certain architectures are more affected by color perturbations.

Abstract

Deep neural networks (DNNs) have have shown state-of-the-art performance for computer vision applications like image classification, segmentation and object detection. Whereas recent advances have shown their vulnerability to manual digital perturbations in the input data, namely adversarial attacks. The accuracy of the networks is significantly affected by the data distribution of their training dataset. Distortions or perturbations on color space of input images generates out-of-distribution data, which make networks more likely to misclassify them. In this work, we propose a color-variation dataset by distorting their RGB color on a subset of the ImageNet with 27 different combinations. The aim of our work is to study the impact of color variation on the performance of DNNs. We perform experiments on several state-of-the-art DNN architectures on the proposed dataset, and the result shows a significant correlation between color variation and loss of accuracy. Furthermore, based on the ResNet50 architecture, we demonstrate some experiments of the performance of recently proposed robust training techniques and strategies, such as Augmix, revisit, and free normalizer, on our proposed dataset. Experimental results indicate that these robust training techniques can improve the robustness of deep networks to color variation.