adversarial example
What It Means
An adversarial example is a deliberately modified input that tricks an AI system into making wrong decisions while appearing normal to humans. Think of it as a digital optical illusion - someone makes tiny, invisible changes to data that cause your AI to completely misinterpret what it's seeing or processing.
Why Chief AI Officers Care
These attacks can cause catastrophic business failures - from self-driving cars misreading stop signs to fraud detection systems approving malicious transactions. They represent a fundamental security vulnerability that traditional cybersecurity tools can't detect, requiring specialized AI defense strategies and potentially exposing companies to liability, regulatory scrutiny, and massive operational disruptions.
Real-World Example
A cybercriminal adds imperceptible noise to a legitimate invoice image that causes your automated accounts payable AI to misread a $1,000 payment as $10,000, or modifies pixels in medical images so subtly that radiologists can't see the changes but the diagnostic AI completely misses a tumor.
Common Confusion
People often think adversarial examples require sophisticated hacking skills or that they're purely theoretical research problems. In reality, many can be generated with simple tools and represent immediate, practical threats to any organization using AI in production systems.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, adversarial examples pose critical safety risks when malicious actors subtly alter medical images, pat...
Finance: In finance, adversarial examples pose significant risks to AI-driven trading algorithms, credit scoring models, and frau...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Machine learning input sample formed by applying a small but intentionally worst-case perturbation ... to a clean example, such that the perturbed input causes a learned model to output an incorrect answer."Source: NISTIR_8269_Draft
"Samples generated from real samples with carefully designed imperceptible perturbations"Source: Zhang,_Yonggang
Related Terms
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