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

Sensitive Region-based Metamorphic Testing Framework using Explainable AI