  Developing consistently well performing visual recognition applications based
on convolutional neural networks, e.g. for autonomous driving, is very
challenging. One of the obstacles during the development is the opaqueness of
their cognitive behaviour. A considerable amount of literature has been
published which describes irrational behaviour of trained CNNs showcasing gaps
in their cognition. In this paper, a methodology is presented that creates
worstcase images using image augmentation techniques. If the CNN's cognitive
performance on such images is weak while the augmentation techniques are
supposedly harmless, a potential gap in the cognition has been found. The
presented worst-case image generator is using adversarial search approaches to
efficiently identify the most challenging image. This is evaluated with the
well-known AlexNet CNN using images depicting a typical driving scenario.
