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. At OpenAI, we think adversarial examples are a good aspect of sec
Adversarial machine learning - Wikipedia
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks.[1] A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.[2] Most machine learning techniques are mostly designed to work on specific problem sets, under the assumption th
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