Lilly Tech Systems
Adversarial Testing for ML
Test ML model robustness against adversarial attacks including perturbation attacks, evasion techniques, and automated adversarial testing.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Adversarial Testing Overview
Introduction to adversarial testing
2. Perturbation Attacks
Understanding perturbation-based attacks
3. Evasion Attack Techniques
Evasion attacks on ML models
4. Robustness Evaluation Metrics
Measuring model robustness
5. Adversarial Training Defense
Defending models with adversarial training
6. Testing Image Classifiers
Adversarial testing for vision models
7. Adversarial Testing Automation
Automating adversarial tests
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of adversarial testing for ml for production AI systems.
Practical Skills
Build hands-on skills with real code examples, frameworks, and tools used by industry professionals.
Best Practices
Apply industry best practices and avoid common pitfalls when implementing testing in your ML projects.
Production Ready
Ship reliable, well-tested AI systems with confidence using automated testing pipelines.