Explanation
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model's performance that may indicate a potential attack.