PluralSight – Adversarial AI Detection And Defense 2025
English | Tutorial | Size: 121.77 MB
Discover how to detect and defend against adversarial attacks on AI systems. This course will teach you essential techniques for identifying manipulated inputs and implementing robust defenses to protect machine learning models in production environments.
What you’ll learn
As adversarial attacks on AI systems become more sophisticated, building effective defenses is crucial for deploying machine learning in security-critical applications. In this course, Adversarial AI: Detection and Defense, you’ll learn practical strategies to protect your models from various attack vectors. First, you’ll explore advanced detection techniques that can identify potentially manipulated inputs before they reach your model. Next, you’ll implement defensive strategies like adversarial training, input preprocessing, and model ensembles that make your systems significantly more robust against attacks. Finally, you’ll learn how to evaluate defense effectiveness through rigorous testing and develop a comprehensive security strategy for your AI systems. When you’re finished with this course, you’ll have the skills and knowledge needed to build machine learning systems that can withstand real-world adversarial threats and maintain reliable performance under attack conditions.
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