论文标题
缩放图像对深神经网络鲁棒性的影响
Impact of Scaled Image on Robustness of Deep Neural Networks
论文作者
论文摘要
深度神经网络（DNN）已被广泛用于计算机视觉任务，例如图像分类，对象检测和分割。尽管最近的研究表明它们易于输入图像中手动数字扰动或失真。网络的准确性受到培训数据集的数据分布的极大影响。缩放原始图像会创建分布数据，这使其成为欺骗网络的对抗性攻击。在这项工作中，我们通过通过不同的倍数将ImageNet挑战数据集的子集缩放来提出一个缩放分数数据集Imagenet-CS。我们工作的目的是研究缩放图像对高级DNN的性能的影响。我们对所提出的Imagenet-CS进行了几个最先进的深神经网络体系结构进行实验，结果显示缩放大小和准确性下降之间存在显着的正相关。此外，根据RESNET50体系结构，我们展示了一些有关拟议的强大训练技术和策略（例如Augmix），在我们提出的Imagenet-CS上免费访问和归一化器（例如Revising and Carlancorizer of Fiemize）的一些测试。实验结果表明，这些强大的训练技术可以改善网络对缩放转换的鲁棒性。
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.
