An adversarial example refers to specially crafted input that is designed to look "normal" to humans but causes misclassification to a machine learning model. Often, a form of specially designed "noise" is …

Jul 23, 2025 · Adversarial machine learning (AML) is refers to machine learning threats which aims to trick machine learning models by providing deceptive input. Such attacks force the machine learning …

Mar 24, 2025 · This NIST Trustworthy and Responsible AI report provides a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is arranged in …

Jul 24, 2024 · Adversarial machine learning (AML) is a field that studies attacks that exploit vulnerabilities in machine learning models and develops defenses to protect against these threats.

Adversarial machine learning is the art of tricking AI systems. The term refers both to threat agents who pursue this art maliciously, as well as the good-intentioned researchers seeking to expose …

Feb 8, 2025 · Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance.

Jan 9, 2026 · This survey offers a comprehensive overview of adversarial machine learning, synthesizing a broad body of research encompassing attack methodologies, defense strategies, and …

Apr 14, 2025 · Adversarial machine learning is a technique that's used to study machine learning (ML) model attacks to identify weak points and form a defense against malicious attacks. Adversarial …

May 3, 2025 · This paper surveys the Adversarial Machine Learning (AML) landscape in modern AI systems, while focusing on the dual aspects of robustness and privacy. Initially, we explore …

Cybersecurity researchers refer to this risk as “adversarial machine learning,” as AI systems can be deceived (by attackers or “adversaries”) into making incorrect assessments.