Data poisoning refers to a method of attack in which an adversary deliberately introduces misleading or harmful data into a machine learning (ML) training dataset. The objective of this malicious act is to compromise the integrity of the ML model, leading to incorrect predictions or classifications when the model is deployed in real-world applications.
In many machine learning systems, the quality and reliability of the training data are crucial for the model’s performance. When an attacker successfully implements data poisoning, they can manipulate the learning process by injecting biased or false information. This can result in a model that performs poorly, behaves unpredictably, or even serves the attacker’s goals by making specific predictions that benefit them.
Data poisoning can take various forms, including:
- Label flipping: Changing the labels of certain data points to mislead the model during training.
- Backdoor attacks: Inserting specific data patterns that cause the model to behave incorrectly only when those patterns are present.
- Outlier addition: Introducing extreme or unusual data points that skew the model’s understanding of the normal data distribution.
Mitigating data poisoning involves multiple strategies, such as robust data validation techniques, anomaly detection, and using diverse training datasets to minimize the impact of any single source of data corruption. Continuous monitoring and updating of models can also help to reduce the risk of data poisoning attacks.