  Deep Learning (DL) is one of the most popular research topics in machine
learning and DL-driven image recognition systems have developed rapidly. Recent
research has employed metamorphic testing (MT) to detect misclassified images.
Most of them discuss metamorphic relations (MR), with limited attention given
to which regions should be transformed. We focus on the fact that there are
sensitive regions where even small transformations can easily change the
prediction results and propose an MT framework that efficiently tests for
regions prone to misclassification by transforming these sensitive regions. Our
evaluation demonstrated that the sensitive regions can be specified by
Explainable AI (XAI) and our framework effectively detects faults.
