  Even though deep neural networks succeed on many different tasks including
semantic segmentation, they lack on robustness against adversarial examples. To
counteract this exploit, often adversarial training is used. However, it is
known that adversarial training with weak adversarial attacks (e.g. using the
Fast Gradient Method) does not improve the robustness against stronger attacks.
Recent research shows that it is possible to increase the robustness of such
single-step methods by choosing an appropriate step size during the training.
Finding such a step size, without increasing the computational effort of
single-step adversarial training, is still an open challenge. In this work we
address the computationally particularly demanding task of semantic
segmentation and propose a new step size control algorithm that increases the
robustness of single-step adversarial training. The proposed algorithm does not
increase the computational effort of single-step adversarial training
considerably and also simplifies training, because it is free of
meta-parameter. We show that the robustness of our approach can compete with
multi-step adversarial training on two popular benchmarks for semantic
segmentation.
