What Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations requires
This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML), which may aid in securing applications of artificial intelligence (AI) against…
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Pillar: AI Governance & Law · Authority: National Institute of Standards and Technology · Version: 1.0.0 · Last updated:
Primary source: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf
SHA-256 integrity: e5644571b146aca64a332beda240fd3b9e53afbc436cdc75fac616e1e441a477
Primary Citations — 8 traced to source
- Section 1: The taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of AI systems by establishing a common language and understanding for the rapidly developing AML landscape.
- Section 2.1.1: Attacks during the ML training stage are called POISONING ATTACKS. In a DATA POISONING attack, an adversary controls a subset of the training data by either inserting or modifying training samples.
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