Testing AI Model Robustness with Adversarial Attacks

AI models are increasingly deployed in critical systems, but they remain vulnerable to subtle, intentional manipulations. Understanding how these models fail is the first step toward building secure, reliable artificial intelligence. This text-based course guides you through the core principles of adversarial machine learning, helping you identify weaknesses in neural networks and implement defensive strategies to secure your models against manipulation. What you'll learn: 1. Understand foundational concepts of adversarial machine learning and why neural networks are vulnerable. 2. Analyze classic perturbation methods like the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). 3. Evaluate model robustness by simulating adversarial inputs in a controlled environment. 4. Explore defense mechanisms, including adversarial training, to harden networks against security threats. 5. Apply modern evaluation practices to verify model reliability before deployment. You will start with essential terminology and the mathematical intuition behind model vulnerabilities, then progress to reading and analyzing conceptual implementations of key attack and defense strategies. This course is designed for developers, data scientists, and security enthusiasts new to adversarial machine learning, requiring only basic programming concepts and no prior security experience. Begin reading today to start building more resilient AI systems.