NIST · ИТ · 5813f83f2b7776617f5a
Poisoning Attacks against Machine Learning: Can Machine Learning be Trustworthy?
Many practical applications benefit from Machine Learning (ML) and Artificial Intelligence (AI) technologies, but their security needs to be studied in more depth before the methods and algorithms are actually deployed in critical settings. In this article
NIST · ИТОфициальный источник · 2022-10-25
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"title_en": "Poisoning Attacks against Machine Learning: Can Machine Learning be Trustworthy?",
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"date": "2022-10-25",
"date_original": "October 25, 2022",
"url": "https://www.nist.gov/publications/poisoning-attacks-against-machine-learning-can-machine-learning-be-trustworthy",
"source": "NIST Publications",
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"authors_en": "Alina Oprea, Anoop Singhal , Apostol Vassilev",
"abstract_excerpt_en": "Many practical applications benefit from Machine Learning (ML) and Artificial Intelligence (AI) technologies, but their security needs to be studied in more depth before the methods and algorithms are actually deployed in critical settings. In this article",
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"title_en": "Poisoning Attacks against Machine Learning: Can Machine Learning be Trustworthy?",
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