From the eBook - 1st edition.
Part 1: Introduction to Adversarial AI
Chapter 1: Getting Started with AI
Types of ML and the ML life cycle
Neural networks and deep learning
Chapter 2: Building Our Adversarial Playground
Setting up your development environment
Creating your virtual environment
Registering your virtual environment with Jupyter notebooks
Verifying your installation
Hands-on basic baseline ML
Developing our target AI service with CNNs
Setup and data collection
Algorithm selection and building the model
Azure Machine Learning services
Chapter 3: Security and Adversarial AI
Securing our adversarial playground
Securing code and artifacts
Securing dependencies with vulnerability scanning
Securing Jupyter Notebooks
Securing models from malicious code
Integrating with DevSecOps and MLOps pipelines
Bypassing security with adversarial AI
Our first adversarial AI attack
Traditional cybersecurity and adversarial AI
Part 2: Model Development Attacks
Chapter 4: Poisoning Attacks
Basics of poisoning attacks
Types of poisoning attacks
Poisoning attack examples
Staging a simple poisoning attack
Creating poisoned samples
Backdoor poisoning attacks
Creating backdoor triggers with ART
Hidden-trigger backdoor attacks
Advanced poisoning attacks
Cybercity defenses with MLOps
Robustness tests against poisoning
Advanced poisoning defenses with ART
Creating a defense strategy
Chapter 5: Model Tampering with Trojan Horses and Model Reprogramming
Injecting backdoors using pickle serialization
Injecting Trojan horses with Keras Lambda layers
Trojan horses with custom layers
