Image classification is a long-standing task in computer vision with deep neuralnetworks (DNN) producing excellent results on various challenges. However, theyare required not only to perform highly accurate on benchmarks such as ImageNet,but also to robustly handle images in adverse conditions, such as modified lighting, sharpness, weather conditions and image compression. Various benchmarksaimed to measure robustness show that neural networks perform differently wellunder distribution shifts. While datasets such as ImageNet-C model for example common corruptions such as blur and adverse weather conditions, we argue thatthe properties of the optical system and the potentially resulting complex lens blurare insufficiently well studied in the literature. This study evaluates the impact ofrealistic optical corruptions on the ImageNet classification. The proposed complexcorruption kernels are direction and wavelength dependent and include chromaticaberration, which are all to be expected in realistic scenarios such as autonomousdriving applications. Our experiments on twelve different DNN models show significant differences of more than 5% in the top1 classification error, when comparedto the model performances on matched ImageNet-C blur kernels.