  As the major factors affecting the safety of deep learning models, corner
cases and related detection are crucial in AI quality assurance for
constructing safety- and security-critical systems. The generic corner case
researches involve two interesting topics. One is to enhance DL models
robustness to corner case data via the adjustment on parameters/structure. The
other is to generate new corner cases for model retraining and improvement.
However, the complex architecture and the huge amount of parameters make the
robust adjustment of DL models not easy, meanwhile it is not possible to
generate all real-world corner cases for DL training. Therefore, this paper
proposes to a simple and novel study aiming at corner case data detection via a
specific metric. This metric is developed on surprise adequacy (SA) which has
advantages on capture data behaviors. Furthermore, targeting at characteristics
of corner case data, three modifications on distanced-based SA are developed
for classification applications in this paper. Consequently, through the
experiment analysis on MNIST data and industrial data, the feasibility and
usefulness of the proposed method on corner case data detection are verified.
