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An input crafted to fool a model into producing a wrong prediction, often by adding small perturbations a human cannot see. Adversarial examples were the first AI security failure mode the research community took seriously and remain the canonical case in robustness work.
Image classification, malware detection, and biometric systems all need adversarial-robustness evaluation. AI red teaming and AI engineering interviews probe this.
An input crafted to fool a model into producing a wrong prediction, often by adding small perturbations a human cannot see. Adversarial examples were the first AI security failure mode the research community took seriously and remain the canonical case in robustness work.
Image classification, malware detection, and biometric systems all need adversarial-robustness evaluation. AI red teaming and AI engineering interviews probe this.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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