Image Confusion Applied to Industrial Defect Detection System

Hao Yuan Chen; Yu Chen Yeh; Makena Lu; Chia Yu Lin

doi:10.1109/ICCE-Taiwan55306.2022.9869058

Image Confusion Applied to Industrial Defect Detection System

Hao Yuan Chen, Yu Chen Yeh, Makena Lu, Chia Yu Lin

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	463-464
Number of pages	2
ISBN (Electronic)	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869058
State	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan Duration: 6 Jul 2022 → 8 Jul 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/Territory	Taiwan
City	Taipei
Period	6/07/22 → 8/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICCE-Taiwan55306.2022.9869058

Cite this

Chen, H. Y., Yeh, Y. C., Lu, M., & Lin, C. Y. (2022). Image Confusion Applied to Industrial Defect Detection System. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (pp. 463-464). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869058

Chen, Hao Yuan ; Yeh, Yu Chen ; Lu, Makena et al. / Image Confusion Applied to Industrial Defect Detection System. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 463-464 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

@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",

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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",

}

Chen, HY, Yeh, YC, Lu, M & Lin, CY 2022, Image Confusion Applied to Industrial Defect Detection System. in Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., pp. 463-464, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, Taiwan, 6/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869058

Image Confusion Applied to Industrial Defect Detection System. / Chen, Hao Yuan; Yeh, Yu Chen; Lu, Makena et al.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 463-464 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Image Confusion Applied to Industrial Defect Detection System

AU - Chen, Hao Yuan

AU - Yeh, Yu Chen

AU - Lu, Makena

AU - Lin, Chia Yu

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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U2 - 10.1109/ICCE-Taiwan55306.2022.9869058

DO - 10.1109/ICCE-Taiwan55306.2022.9869058

M3 - 會議論文篇章

AN - SCOPUS:85138740113

T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

SP - 463

EP - 464

BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Y2 - 6 July 2022 through 8 July 2022

ER -

Chen HY, Yeh YC, Lu M, Lin CY. Image Confusion Applied to Industrial Defect Detection System. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 463-464. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869058

Image Confusion Applied to Industrial Defect Detection System

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