Adversarial machine learning

Adversarial machine learning is a machine learning technique that attempts to fool models by supplying deceptive input. The most common reason is to cause a malfunction in a machine learning model. Most machine learning techniques were designed to work on specific problem sets in which the training and test data are generated from the same statistical distribution (IID). When those models are applied to the real world, adversaries may supply data that violates that statistical assumption. This data may be arranged to exploit specific vulnerabilities and compromise the results.