Machine Learning Security

Machine learning security refers to the practices and techniques used to protect machine learning models and systems from various types of attacks and threats, such as data poisoning, model inversion, and adversarial examples. As machine learning becomes increasingly integral to critical applications and decision-making processes, ensuring the security of these systems is crucial to preventing potential vulnerabilities and maintaining trust in AI-driven technologies.