  Deep neural networks (DNNs) have been widely used in computer vision tasks
like image classification, object detection and segmentation. Whereas recent
studies have shown their vulnerability to manual digital perturbations or
distortion in the input images. The accuracy of the networks is remarkably
influenced by the data distribution of their training dataset. Scaling the raw
images creates out-of-distribution data, which makes it a possible adversarial
attack to fool the networks. In this work, we propose a Scaling-distortion
dataset ImageNet-CS by Scaling a subset of the ImageNet Challenge dataset by
different multiples. The aim of our work is to study the impact of scaled
images on the performance of advanced DNNs. We perform experiments on several
state-of-the-art deep neural network architectures on the proposed ImageNet-CS,
and the results show a significant positive correlation between scaling size
and accuracy decline. Moreover, based on ResNet50 architecture, we demonstrate
some tests on the performance of recent proposed robust training techniques and
strategies like Augmix, Revisiting and Normalizer Free on our proposed
ImageNet-CS. Experiment results have shown that these robust training
techniques can improve networks' robustness to scaling transformation.
