@inproceedings{672abf1512db4be88635b62eab7f5476,
title = "Image Confusion Applied to Industrial Defect Detection System",
abstract = "There have been many related security issues about Artificial Intelligence (AI) in recent years. During the manufacturing process, products are captured by images for defect detection. If attackers use the model inversion attack to attack the AI model, the input image can be roughly restored, resulting in product information leakage. In this paper, we propose a system that confuses input images and uses them to train the model. Experiments show that our model has a high accuracy of 94.4% in defect image classification. Thus, the proposed system can achieve product information protection and accurate defect detection.",
author = "Chen, {Hao Yuan} and Yeh, {Yu Chen} and Makena Lu and Lin, {Chia Yu}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",
year = "2022",
doi = "10.1109/ICCE-Taiwan55306.2022.9869058",
language = "???core.languages.en_GB???",
series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "463--464",
booktitle = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",
}
