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.

翻译：深度神经网络（DNNs）在图像分类、分割与目标检测等计算机视觉应用中展现出卓越性能。然而近年研究发现，输入数据中的人为数字扰动（即对抗攻击）会暴露出其脆弱性。网络准确率显著受限于训练数据分布特性。输入图像色彩空间的失真或扰动会产生分布外数据，导致网络更易产生误判。本研究通过在ImageNet子集上以27种不同组合扭曲RGB颜色，构建颜色变异数据集。我们旨在探究颜色变异对DNN性能的影响，并在所提数据集上对多种前沿DNN架构进行实验。结果表明颜色变异与准确率损失之间存在显著相关性。此外，基于ResNet50架构，我们测试了近期提出的鲁棒训练技术（如Augmix、revisit与free normalizer）在所提数据集上的表现。实验证实这些鲁棒训练方法能有效提升深度网络对颜色变异的鲁棒性。