- Preface
- I. An Introduction to Fooling AI
- 1. Introduction
- 2. Attack Motivations
- 3. Deep Neural Network (DNN) Fundamentals
- 4. DNN Processing for Image, Audio, and Video
- II. Generating Adversarial Input
- 5. The Principles of Adversarial Input
- 6. Methods for Generating Adversarial Perturbation
- III. Understanding the Real-World Threat
- 7. Attack Patterns for Real-World Systems
- 8. Physical-World Attacks
- IV. Defense
- 9. Evaluating Model Robustness to Adversarial Inputs
- 10. Defending Against Adversarial Inputs
- 11. Future Trends: Toward Robust AI
- A. Mathematics Terminology Reference
- Index
About the Author
Katy Warr gained her degree in AI and computer science from the University of Edinburgh at a time when there was insufficient compute power and data available for deep learning to be much more than a theoretical pursuit. Following many years developing enterprise software, she now specializes in AI and considers herself fortunate to be part of this exciting field as it becomes an integral part of everyday life.
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