Adversarial learning is a subfield of machine learning that involves training models to recognize and defend against malicious attacks or manipulations. Adversarial attacks involve intentionally perturbing or altering input data to cause a model to produce incorrect or unintended outputs. Adversarial learning techniques aim to improve the robustness and security of machine learning models by exposing them to a range of adversarial inputs and training them to recognize and resist these attacks. Adversarial learning can be used in a variety of applications, such as fraud detection, cybersecurity, and image recognition. As the use of machine learning becomes more widespread, the development of effective adversarial learning techniques is becoming increasingly important to ensure the reliability and security of these systems.