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.

翻译：深度神经网络（DNNs）已广泛应用于图像分类、目标检测和分割等计算机视觉任务。然而，近期研究表明它们对输入图像中的人为数字扰动或失真存在脆弱性。网络的准确性受其训练数据集数据分布的显著影响。对原始图像进行缩放会产生分布外数据，这可能成为一种欺骗网络的对抗攻击手段。在本工作中，我们通过对ImageNet挑战数据集的一个子集进行不同倍数的缩放，构建了缩放失真数据集ImageNet-CS。本研究的目的是探究缩放图像对先进DNN性能的影响。我们在所提出的ImageNet-CS上对多种最先进的深度神经网络架构进行了实验，结果显示缩放尺寸与准确率下降之间存在显著正相关性。此外，基于ResNet50架构，我们测试了近期提出的若干鲁棒训练技术及策略（如Augmix、Revisiting和Normalizer Free）在所提ImageNet-CS上的性能。实验结果表明，这些鲁棒训练技术能够提升网络对缩放变换的鲁棒性。