Research on Detection Methods for Adversarial Capability of Artificial Intelligence Software

Artificial intelligence software faces more diverse network security attacks, and its Adversarial ability is crucial. This paper studies the Adversarial ability detection method of AI software and its application in the field of medical software, and proposes an AI software attack test specification scheme for the problem of difficult to measure the strength of the attack test, which provides the testers with the reference ideas for the management of the attack test.

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Publication Details

Published
2024-12-06
DOI
https://doi.org/10.1109/iaecst64597.2024.11117374
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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Research on Detection Methods for Adversarial Capability of Artificial Intelligence Software

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Adversarial Robustness in Machine Learning
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Research on Detection Methods for Adversarial Capability of Artificial Intelligence Software

Jielin Cheng, Tinghua Zhang, Yao Nie, Yanhong Chen, Yanjiao Ma, Manshuang She
article en

Abstract

Artificial intelligence software faces more diverse network security attacks, and its Adversarial ability is crucial. This paper studies the Adversarial ability detection method of AI software and its application in the field of medical software, and proposes an AI software attack test specification scheme for the problem of difficult to measure the strength of the attack test, which provides the testers with the reference ideas for the management of the attack test.

Ministry of Industry and Information Technology (CN)
Openalex Percentile: Top 72%
Adversarial Robustness in Machine Learning
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