User:Jolsen1022/Adversarial machine learning

This is my wiki page on Adversarial Machine Learning

Adversarial Machine Learning refers to a subfield of artificial intelligence (AI) and machine learning (ML) that focuses on understanding and mitigating vulnerabilities in machine learning models against adversarial attacks. Adversarial attacks involve manipulating input data in a way that is often imperceptible to humans but can lead to misclassification or incorrect behavior of machine learning models.

Machine learning models are susceptible to adversarial attacks due to their reliance on patterns and features present in the training data. Adversarial examples are crafted inputs designed to exploit these vulnerabilities, causing the model to produce incorrect outputs. The study of adversarial machine learning aims to understand the nature of these attacks, develop robust models, and design defense mechanisms to enhance the security of machine learning systems.