Machine Learning Defense

Machine learning defense refers to the techniques and strategies used to protect machine learning models from adversarial attacks, data poisoning, and other types of malicious activity that can compromise their performance and reliability. As machine learning becomes increasingly integral to various industries, including security, finance, and healthcare, developing effective defenses against these threats is crucial to ensuring the trustworthiness and robustness of AI systems.