Adversarial Machine Learning

Deep dive into the science of adversarial attacks and defenses for machine learning. Learn the mathematics and implementation of FGSM, PGD, and C&W attacks, understand data poisoning and model inversion, build robust defenses with adversarial training and certified robustness, and use the Adversarial Robustness Toolbox (ART) for practical experimentation.

Start Course → Evasion Attacks
7
Lessons
40+
Examples
~4hr
Total Time
📈
Research-Grade

What You'll Learn

This course provides research-depth coverage of adversarial ML, from foundational attacks to state-of-the-art defenses.

⚡

Attack Techniques

Master FGSM, PGD, C&W, and other evasion attacks. Understand gradient-based, score-based, and decision-based attack paradigms.

💣

Data Poisoning

Learn backdoor attacks, label flipping, and clean-label poisoning techniques that corrupt models during training.

🛡

Defense Strategies

Implement adversarial training, defensive distillation, input preprocessing, and certified robustness defenses.

🛠

ART Library

Hands-on practice with IBM's Adversarial Robustness Toolbox for implementing attacks and defenses.

Course Lessons

Follow the lessons in order for a comprehensive understanding of adversarial machine learning.

Beginner

1. Introduction

What is adversarial ML? History, taxonomy of attacks, threat models, and why neural networks are vulnerable to adversarial examples.

15 min read →
 Intermediate

2. Evasion Attacks

Deep dive into FGSM, PGD, C&W, DeepFool, and other evasion attacks. Learn the math, implementation, and practical considerations.

30 min read →
 Intermediate

3. Poisoning Attacks

Training-time attacks: backdoor injection, label flipping, clean-label poisoning, and federated learning poisoning.

25 min read →
 Intermediate

4. Model Inversion

Privacy attacks: model inversion, membership inference, attribute inference, and training data extraction from LLMs.

20 min read →
 Advanced

5. Defenses

Defensive techniques: adversarial training, defensive distillation, input transformation, ensemble methods, and detection.

25 min read →
 Advanced

6. Robustness

Certified defenses: randomized smoothing, interval bound propagation, Lipschitz constraints, and formal verification.

25 min read →
 Advanced

7. Best Practices

Putting it all together: evaluation protocols, benchmarking, responsible research, and deploying robust models in production.

15 min read →

Prerequisites

What you need before starting this course.

Before You Begin:
  • Solid understanding of deep learning (neural networks, backpropagation, loss functions)
  • Proficiency in Python and PyTorch or TensorFlow
  • Basic knowledge of calculus and linear algebra
  • Familiarity with image classification tasks