Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection. However, recent research has revealed a vulnerability in advanced DNNs when faced with deliberate manipulations of input data, known as adversarial attacks. Moreover, the accuracy of DNNs is heavily influenced by the distribution of the training dataset. Distortions or perturbations in the color space of input images can introduce out-of-distribution data, resulting in misclassification. In this work, we propose a brightness-variation dataset, which incorporates 24 distinct brightness levels for each image within a subset of ImageNet. This dataset enables us to simulate the effects of light and shadow on the images, so as is to investigate the impact of light and shadow on the performance of DNNs. In our study, we conduct experiments using several state-of-the-art DNN architectures on the aforementioned dataset. Through our analysis, we discover a noteworthy positive correlation between the brightness levels and the loss of accuracy in DNNs. Furthermore, we assess the effectiveness of recently proposed robust training techniques and strategies, including AugMix, Revisit, and Free Normalizer, using the ResNet50 architecture on our brightness-variation dataset. Our experimental results demonstrate that these techniques can enhance the robustness of DNNs against brightness variation, leading to improved performance when dealing with images exhibiting varying brightness levels.

翻译：深度神经网络（DNN）在图像分类、分割及目标检测等各类计算机视觉任务中取得了显著进展。然而，最新研究表明，面对输入数据的刻意操控（即对抗性攻击）时，先进DNN存在脆弱性。此外，DNN的准确性受训练数据集分布影响显著。输入图像色彩空间中的失真或扰动可能引入分布外数据，导致错误分类。本文提出一个亮度变化数据集，该数据集针对ImageNet子集中的每张图像包含24种不同亮度等级。该数据集使我们能够模拟光照与阴影对图像的影响，进而探究光照与阴影对DNN性能的作用。本研究采用多种最先进的DNN架构在所述数据集上进行实验。通过分析，我们发现亮度等级与DNN准确率下降之间存在显著正相关关系。此外，我们使用ResNet50架构在亮度变化数据集上评估了近期提出的鲁棒训练技术与策略（包括AugMix、Revisit和Free Normalizer）的有效性。实验结果表明，这些技术能够提升DNN对亮度变化的鲁棒性，从而在处理具有不同亮度等级的图像时表现出更优性能。