Sensitive Region-based Metamorphic Testing Framework using Explainable AI
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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 used metamorphic testing (MT) to detect misclassified images. Most of them discuss metamorphic relations (MR), with little discussion on which regions should be transformed. We focus on the fact that there are sensitive regions where even a small transformation can easily change the prediction results and propose an MT framework that efficiently tests for regions prone to misclassification by transforming the sensitive regions. Our evaluation showed that the sensitive regions can be specified by Explainable AI (XAI) and our framework effectively detects faults.
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