Radial distortion correction for a single image is an often overlooked problem in computer vision. It is possible to rectify images accurately when the camera and lens are known or physically available to take additional images with a calibration pattern. However, some- times it is impossible to identify the type of camera or lens of an image, e.g. crowd-sourced datasets. Nonetheless, it is still important to cor- rect that image for radial distortion in these cases. Especially in the last few years, solving the radial distortion correction problem from a single image with a deep neural network approach increased in popular- ity. This paper shows that these approaches tend to overfit completely on the synthetic data generation process used to train such networks. Additionally, we investigate which parts of this process are responsi- ble for overfitting, and apply an explainability tool to further investi- gate the behavior of the trained models. Furthermore, we introduce a new dataset based on the popular ImageNet dataset as a new bench- mark for comparison. Lastly, we propose a efficient solution to the over- fitting problem by feeding edge images to the neural networks instead of the images. Source code, data, and models are publicly available at https://github.com/cvjena/deeprect.

In a time where the training of new machine learning models is extremely time-consuming and resource-intensive and the sale of these models or the access to them is more popular than ever, it is important to think about ways to ensure the protection of these models against theft. In this paper, we present a method for estimating the similarity or distance between two black-box models. Our approach does not depend on the knowledge about specific training data and therefore may be used to identify copies of or stolen machine learning models. It can also be applied to detect instances of license violations regarding the use of datasets. We validate our proposed method empirically on the CIFAR-10 and MNIST datasets using convolutional neural networks, generative adversarial networks and support vector machines. We show that it can clearly distinguish between models trained on different datasets. Theoretical foundations of our work are also given.