Adversarial Machine Learning is a field that studies how machine learning models can be fooled by intentionally crafted inputs, known as adversarial examples. These inputs are designed to mislead the model into making incorrect predictions, highlighting vulnerabilities in the algorithms used for tasks like image recognition or natural language processing. Researchers in this area aim to understand these weaknesses and develop techniques to make models more robust against such attacks. This involves creating better training methods and defensive strategies to ensure that models remain accurate even when faced with deceptive inputs, ultimately improving the security of systems that rely on machine learning.