Adversarial Machine Learning (AML) is a type of machine learning where two neural networks compete against each other. The first network tries to classify images into one category, while the second network tries to fool it by creating fake examples. This process creates a feedback loop between the two networks, which helps them both improve at the same time.

The goal of adversarial machine learning is to build systems that can detect and prevent attacks based on their knowledge. This includes detecting attacks that are already known (white hat) and unknown (black hat).

The most common example of this is the smurf attack, where a hacker sends a large number of messages to a server, causing it to crash. This is done because the attacker wants to overload the server so that it cannot respond to legitimate users.

More info: What is Observability