Adversarial Model Defense for Labs : Hardening AI Against Attacks with 25 Practical Exercises and Strategies

In an era where AI powers everything from autonomous vehicles to medical diagnostics, one invisible threat could unravel it all: adversarial attacks. These subtle manipulations can fool even the most sophisticated models, leading to catastrophic failures. But what if you could arm your AI systems with unbreakable defenses?
Enter Adversarial Model Defense for Labs: Hardening AI Against Attacks with 25 Practical Exercises and Strategies by cybersecurity expert Cyrus Laban. This groundbreaking guide is your hands-on blueprint for transforming vulnerable AI into resilient fortresses. Drawing from the latest in adversarial machine learning research, Laban demystifies the "cat-and-mouse" game between attackers and defenders, equipping you with proven techniques to safeguard models across computer vision, NLP, reinforcement learning, and beyond.
Dive into a structured journey across five comprehensive parts:

• Introduction to Adversarial AI: Grasp the fundamentals, from evasion and poisoning attacks to real-world threats like manipulated traffic signs or poisoned datasets.
• Foundational Defense Techniques: Master data sanitization, adversarial training (using FGSM and PGD), and regularization to build robust baselines.
• Advanced Defense Strategies: Explore cutting-edge methods like defensive distillation, feature squeezing, ensemble models, and certified defenses for provable security.
• Real-World Applications and Case Studies: Apply defenses to high-stakes scenarios, including securing YOLO for object detection, hardening sentiment analysis, and protecting RL agents in gaming.
• Building a Secure AI Pipeline: Learn monitoring with SHAP and LIME, secure deployment best practices, and ethical considerations for fair, future-proof AI.

What sets this book apart? 25 practical exercises that turn theory into action—simulate evasion attacks, generate adversarial examples, implement robust training pipelines, and more. Whether you're a data scientist, AI engineer, or security professional, these lab-style challenges (complete with Python code snippets and tools like PyTorch, TensorFlow, and scikit-learn) let you experiment in a safe environment, honing skills that translate directly to your projects.
Packed with case studies, glossaries, Python library recommendations, and additional resources, this 2025 first edition bridges the gap between academic concepts and deployable solutions.