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IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service
IEEE Std 3129-2023
Year: 2023
IEEE - The Institute of Electrical and Electronics Engineers, Inc.
Abstract: Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.
Subject: adversarial attacks
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IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service
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| contributor author | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| date accessioned | 2024-12-17T08:09:19Z | |
| date available | 2024-12-17T08:09:19Z | |
| date copyright | 02 June 2023 | |
| date issued | 2023 | |
| identifier other | 10141539.pdf | |
| identifier uri | http://yse.yabesh.ir/std;query=autho47037DAVY%20-%20login/handle/yse/336350 | |
| description abstract | Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard. | |
| language | English | |
| publisher | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| title | IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service | en |
| title | IEEE Std 3129-2023 | num |
| type | standard | |
| page | 34 | |
| status | Active | |
| tree | IEEE - The Institute of Electrical and Electronics Engineers, Inc.:;2023 | |
| contenttype | fulltext | |
| subject keywords | adversarial attacks | |
| subject keywords | robustness | |
| subject keywords | assessment framework | |
| subject keywords | artificial Intelligence-based services | |
| subject keywords | common corruption | |
| subject keywords | IEEE 3129 | |
| identifier DOI | 10.1109/IEEESTD.2023.10141539 |