  Progress in making neural networks more robust against adversarial attacks is
mostly marginal, despite the great efforts of the research community. Moreover,
the robustness evaluation is often imprecise, making it difficult to identify
promising approaches. We analyze the classification decisions of 19 different
state-of-the-art neural networks trained to be robust against adversarial
attacks. Our findings suggest that current untargeted adversarial attacks
induce misclassification towards only a limited amount of different classes.
Additionally, we observe that both over- and under-confidence in model
predictions result in an inaccurate assessment of model robustness. Based on
these observations, we propose a novel loss function for adversarial attacks
that consistently improves attack success rate compared to prior loss functions
for 19 out of 19 analyzed models.
