Adversarial Machine Learning

Adversarial machine learning refers to the study of how machine learning models can be manipulated or deceived by intentionally crafted input data, known as adversarial examples. As AI becomes increasingly pervasive in critical applications, understanding adversarial machine learning is crucial for developing robust and secure models that can withstand potential attacks, making it a vital area of research for the tech community to ensure the reliability and trustworthiness of AI systems.