Summary
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake, like optical illusions for machines.
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These examples are instances with small, intentional feature perturbations that cause a machine learning model to make a false prediction.
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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]
Adversarial machine learning - Wikipedia
wikipedia.org
A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014.[1] Two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.
Generative adversarial network - Wikipedia
wikipedia.org
Indeed, adversarial use goes well beyond this simple classification example: forensic analysis of malware which incorporates clustering, anomaly detection, and ...
Adversarial machine learning tutorial
aaai18adversarial.github.io
