According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the word “Adversarial Machine Learning” means a practice concerned with the design of ML algorithms that can resist security challenges, the study of the capabilities of attackers, and the understanding of attack consequences. Inputs in adversarial ML are purposely designed to make a mistake in its predictions despite resembling a valid input to a human.
