Textbook in PDF format

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data.
Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you.
Delve into DNNs and discover how they could be tricked by adversarial input
Investigate methods used to generate adversarial input capable of fooling DNNs
Explore real-world scenarios and model the adversarial threat
Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data
Examine some ways in which AI might become better at mimicking human perception in years to come
Table of Contents
An Introduction to Fooling AI
Introduction
Attack Motivations
Deep Neural Network (DNN) Fundamentals
DNN Processing for Image, Audio, and Video
Generating Adversarial Input
The Principles of Adversarial Input
Methods for Generating Adversarial Perturbation
Understanding the Real-World Threat
Attack Patterns for Real-World Systems
Physical-World Attacks
Defense
Evaluating Model Robustness to Adversarial Inputs
Defending Against Adversarial Inputs
Future Trends: Toward Robust AI
Mathematics Terminology Reference