  In this paper, we propose a new data augmentation method, Random Shadows and
Highlights (RSH) to acquire robustness against lighting perturbations. Our
method creates random shadows and highlights on images, thus challenging the
neural network during the learning process such that it acquires immunity
against such input corruptions in real world applications. It is a
parameter-learning free method which can be integrated into most vision related
learning applications effortlessly. With extensive experimentation, we
demonstrate that RSH not only increases the robustness of the models against
lighting perturbations, but also reduces over-fitting significantly. Thus RSH
should be considered essential for all vision related learning systems. Code is
available at: https://github.com/OsamaMazhar/Random-Shadows-Highlights.
