Adversarial machine learning

Machine learning and data mining
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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks.

Machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID). However, this assumption is often dangerously violated in practical high-stake applications, where users may intentionally supply fabricated data that violates the statistical assumption.

Most common attacks in adversarial machine learning include evasion attacks, data poisoning attacks, Byzantine attacks and model extraction.