Abstract
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.
Abstract (translated)
深度神经网络(DNN)在多种计算机视觉任务中取得了显著的进展，包括图像分类、分割和物体检测。然而，最近的研究揭示了高级DNN在面临有意地对输入数据进行操作的攻击时的一个脆弱性，这种攻击称为对抗攻击。此外，DNN的准确性受到训练数据分布的强烈影响。输入图像的颜色空间中的扭曲或扰动可以引入不在分布的数据，导致分类错误。在本文中，我们提出了一个亮度变化数据集，该数据集包括ImageNet中每个图像的24个不同亮度水平。这个数据集使我们能够模拟光和暗对图像的影响，以研究光和暗对DNN性能的影响。在我们的研究中，我们使用了几个先进的DNN架构在上文提到的数据集上进行实验。通过我们的分析，我们发现了DNN亮度水平与准确性损失之间存在显著 positive correllation。此外，我们评估了最近提出的 robust 训练技术和策略，包括 Aug Mix、Revisit和Free Normalizer，使用ResNet50架构在我们的亮度变化数据集上。我们的实验结果表明，这些技术可以增强DNN对亮度变化的抵抗力，导致处理具有不同亮度水平的图像时性能得到改善。
URL
https://arxiv.org/abs/2305.14165
