Adversarial Machine Learning

Adversarial Machine Learning refers to a specialized field within artificial intelligence focused on developing techniques that enable AI models to withstand and resist malicious attacks. This area of study addresses the vulnerabilities of machine learning systems by designing robust algorithms capable of identifying and mitigating adversarial inputs data specifically crafted to deceive or exploit the weaknesses in a model. These attacks can lead to erroneous predictions or system failures, posing significant risks in various applications, from autonomous vehicles to financial forecasting. Adversarial machine learning aims to enhance the security and reliability of AI systems by employing defensive strategies such as adversarial training, defensive distillation, and robust optimization techniques.