{
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  "@id": "https://anchorfact.org/kb/adversarial-machine-learning",
  "headline": "Adversarial Machine Learning: Attacks, Defenses, and Robustness Engineering",
  "description": "Adversarial Machine Learning studies how AI systems can be fooled — and defended. Tiny perturbations invisible to humans can cause state-of-the-art models to misclassify with high confidence. Building robust AI requires understanding the attack surface and engineering defenses.",
  "dateCreated": "2026-05-24T02:49:13.464Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
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      "name": "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e3)",
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      "name": "A survey on adversarial machine learning: Attacks, defenses, real-world applications, and algorithmic framework",
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