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Adversarial AI Attacks, Mitigations, and Defense Strategies

You're reading from   Adversarial AI Attacks, Mitigations, and Defense Strategies A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835087985
Length 586 pages
Edition 1st Edition
Languages
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Author (1):
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John Sotiropoulos John Sotiropoulos
Author Profile Icon John Sotiropoulos
John Sotiropoulos
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Toc

Table of Contents (27) Chapters Close

Preface 1. Part 1: Introduction to Adversarial AI FREE CHAPTER
2. Chapter 1: Getting Started with AI 3. Chapter 2: Building Our Adversarial Playground 4. Chapter 3: Security and Adversarial AI 5. Part 2: Model Development Attacks
6. Chapter 4: Poisoning Attacks 7. Chapter 5: Model Tampering with Trojan Horses and Model Reprogramming 8. Chapter 6: Supply Chain Attacks and Adversarial AI 9. Part 3: Attacks on Deployed AI
10. Chapter 7: Evasion Attacks against Deployed AI 11. Chapter 8: Privacy Attacks – Stealing Models 12. Chapter 9: Privacy Attacks – Stealing Data 13. Chapter 10: Privacy-Preserving AI 14. Part 4: Generative AI and Adversarial Attacks
15. Chapter 11: Generative AI – A New Frontier 16. Chapter 12: Weaponizing GANs for Deepfakes and Adversarial Attacks 17. Chapter 13: LLM Foundations for Adversarial AI 18. Chapter 14: Adversarial Attacks with Prompts 19. Chapter 15: Poisoning Attacks and LLMs 20. Chapter 16: Advanced Generative AI Scenarios 21. Part 5: Secure-by-Design AI and MLSecOps
22. Chapter 17: Secure by Design and Trustworthy AI 23. Chapter 18: AI Security with MLSecOps 24. Chapter 19: Maturing AI Security 25. Index 26. Other Books You May Enjoy

Preface

The rise of AI is a new revolution in the making, transforming our lives. Alongside the phenomenal opportunities, new risks and threats are emerging, especially in the area of security, and new skills are demanded to safeguard AI systems. This is because some of these threats manipulate the very essence of how AI works to trick AI systems. We call this adversarial AI, and this book will walk you through techniques, examples, and countermeasures. We will explore them from both offensive and defensive perspectives; we will act as an attacker, staging attacks to demonstrate the threats and then discussing how to mitigate them.

Understanding adversarial AI and defending against it poses new challenges for cybersecurity professionals because they require an understanding of AI and Machine Learning (ML) techniques. The book assumes you have no ML or AI expertise, which will be true for most cybersecurity professionals. Although it will not make you a data scientist, the book will help you build a foundational hands-on understanding of ML and AI, enough to understand and detect adversarial AI attacks and defend against them.

AI has evolved. Its first wave covered predictive (or discriminative) AI with models classifying or predicting values from inputs. This is now mainstream, and we use it every day on our smartphones, for passport checks, at hospitals, and with home assistants. We will cover attacks on this strand of AI before we move to the next frontier of AI, generative AI, which creates new content. We will cover Generative Adversarial Networks (GANs), deepfakes, and the new revolution of Large Language Models (LLMs) such as ChatGPT.

The book strives to be hands-on, but adversarial AI is an evolving research topic. Thousands of research papers have been published detailing experiments in lab conditions. We will try to group this research into concrete themes while providing plenty of references for you to dive into for more details.

We will wrap up our journey with a methodology for secure-by-design AI with core elements such as threat modeling and MLSecOps, while looking at Trustworthy AI.

The book is detailed and demanding at times, asking for your full attention. The reward, however, is high. You will gain an in-depth understanding of AI and its advanced security challenges. In our changing times, this is essential to safeguard AI against its abusers.

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