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Training Camp • Cybersecurity Glossary
Adversarial examples are subtly perturbed inputs crafted to fool machine learning models into wrong predictions, exposing neural network robustness gaps.
Adversarial Examples Definition: Adversarial examples are subtly perturbed inputs crafted to fool machine learning models into wrong predictions, exposing neural network robustness gaps.
Adversarial examples are inputs to a machine learning model that have been deliberately perturbed with small, often imperceptible changes designed to cause the model to make an incorrect prediction. A classic case is altering a few pixels of an image so a classifier misidentifies it with high confidence, while a human still sees the original object. They expose fundamental robustness weaknesses in neural networks and are a central concern in adversarial machine learning and AI security.
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