Impact of Light and Shadow on Robustness of Deep Neural Networks
Chengyin Hu Weiwen Shi Chao Li Jialiang Sun Donghua Wang Junqi Wu Guijian Tang
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.