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Course
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Introduction to Adversarial AI
Discover how adversarial attacks can compromise even the most sophisticated AI systems. This course will teach you how to identify, understand, and simulate key attack vectors that threaten machine learning models in production environments.
What you'll learn
Machine learning models are increasingly being deployed in critical applications, yet they remain vulnerable to subtle manipulations that can cause dramatic failures. In this course, Introduction to Adversarial AI, you'll learn to identify and understand the primary ways adversaries can attack modern AI systems. First, you'll explore the fundamental concepts behind adversarial examples, including perturbations, evasion attacks, and poisoning techniques. Next, you'll discover how to use industry-standard tools like CleverHans and ART to simulate real attacks on neural networks. Finally, you'll learn how black-box models can be reverse-engineered through model extraction techniques. When you're finished with this course, you'll have the skills and knowledge of adversarial AI needed to better understand the security vulnerabilities in your machine learning systems and take the first steps toward protecting them.
Table of contents
About the author
Dr. Goran Trajkovski is a seasoned professional with over 30 years of experience in AI, data science, and learning design, focused on innovative strategies and effective leadership.
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