What is data poisoning and how does it affect AI models?

What is data poisoning and how does it affect AI models?

Data poisoning is a type of attack where malicious data is injected into the training set of an AI model, leading to incorrect predictions or behaviors.

Can you give me an example of how data poisoning might occur?

Sure! For instance, if a spam filter is trained on user-reported emails, an attacker might submit false reports to skew the model's understanding of what constitutes spam.

What are the potential consequences of data poisoning?

Consequences can include reduced model accuracy, biased outcomes, and even security vulnerabilities, which can be exploited by attackers.

How can we protect AI models from data poisoning?

Protecting AI models involves techniques like data validation, anomaly detection, and using robust training methods to minimize the impact of malicious data.