  The paper proposes a new testing approach for Deep Neural Networks (DNN)
using gradient-free optimization to find perturbation chains that successfully
falsify the tested DNN, going beyond existing grid-based or combinatorial
testing. Applying it to an image segmentation task of detecting railway tracks
in images, we demonstrate that the approach can successfully identify
weaknesses of the tested DNN regarding particular combinations of common
perturbations (e.g., rain, fog, blur, noise) on specific clusters of test
images.
