OpenAI Blog · Feb 24, 2017
Attacking machine learning with adversarial examples
Reviewed by Errol Vogt, Site support technician & online learning analyst · original summary · editorial policy
Attacking machine learning with adversarial examples. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. This update is relevant for small-office operators tracking changes in their tools.
Operator takeaway: For operators: review whether 'Attacking machine learning with adversarial examples' affects your current setup before relying on it in production.
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