Indic Pacific Glossary

Adversarial Machine Learning

Date of Addition

22 Mar 2025

A technique used to study machine learning model vulnerabilities by creating deceptive inputs designed to cause AI systems to malfunction or make incorrect predictions. It involves both offensive mechanisms (creating adversarial examples) and defensive approaches (building more robust models).

Adversarial machine learning operates by manipulating input data in ways imperceptible to humans but that cause dramatic changes in model outputs. Defensemple, adding carefully calculated noise to an image of a panda can make a sophisticated image classifier confidently misidentify it as a gibbon. Defence mechanisms include adversarial training (exposing models to adversarial examples during training) and ensemble methods that combine multiple models to improve robustness against attacks.

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