Adversarial Machine Learning is a collection of techniques to train neural networks on how to spot intentionally misleading data or behaviors. This differs from the standard classification problem in machine learning, since the goal is not just to spot “bad” inputs, but preemptively locate vulnerabilities and craft more flexible learning algorithms.
