Adversarial Machine Learning

Adversarial machine learning studies how attackers can exploit weaknesses in machine learning models and how those vulnerabilities can be prevented. Common attacks include adversarial examples, data poisoning, model evasion, and model extraction. Researchers and security teams use defensive techniques such as adversarial training, input validation, and robust model design to strengthen AI systems against these threats.

As AI becomes part of critical business and security operations, protecting models from manipulation is essential. Adversarial machine learning helps organizations build trustworthy AI systems that remain reliable even when exposed to malicious inputs, reducing the risk of inaccurate predictions, security breaches, and operational failures.