Andmepitis

Andmepitis, also known as data poisoning, is a malicious technique used to corrupt a dataset, often with the intention of compromising machine learning models trained on that data. This can be achieved by introducing errors, biases, or irrelevant information into the dataset. The goal is to manipulate the model's behavior, leading to inaccurate predictions or decisions. Data poisoning can be particularly harmful in critical applications such as healthcare, finance, and security, where the integrity of data is paramount. Detecting and mitigating data poisoning is a challenging task, as it often requires sophisticated techniques and continuous monitoring of the data and model performance. Researchers and practitioners are actively developing methods to identify and counteract data poisoning to ensure the reliability and security of machine learning systems.