Sensitive Region-based Metamorphic Testing Framework using Explainable AI

Autor:	Torikoshi, Yuma, Nishi, Yasuharu, Takahashi, Juichi
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.07580 Zobrazit plný text záznamu View this record from Arxiv