How can you protect your ML model from data poisoning attacks?
Data poisoning attacks are a type of adversarial attack that aim to degrade the performance or compromise the security of a machine learning (ML) model by manipulating its training or test data. These attacks can have serious consequences, such as producing inaccurate predictions, leaking sensitive information, or violating ethical principles. Therefore, it is crucial to protect your ML model from data poisoning attacks by applying some defensive strategies. In this article, you will learn about some common types of data poisoning attacks and how to prevent or mitigate them.