Data Poisoning in Machine Learning: Why and How People Manipulate Training Data

Data poisoning attacks in machine learning involve intentionally corrupting training datasets to degrade model performance or inject malicious behaviors. This article explores why attackers manipulate training data and the methods they employ. Understanding data poisoning is critical for AI security, as models trained on contaminated data can make unreliable predictions or exhibit unexpected biases.