Strengthening Deep Neural Networks Making AI Less Susceptible to Adversarial Trickery 1st Edition Katy Warr 1492044903 9781492044901

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Product details:

• ISBN 10: 1492044903
• ISBN 13: 9781492044901
• Author: Katy Warr

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:

1. Introduction

2. Attack Motivations

3. Deep Neural Network (DNN) Fundamentals

4. DNN Processing for Image, Audio, and Video

5. The Principles of Adversarial Input

6. Methods for Generating Adversarial Perturbation

7. Attack Patterns for Real-World Systems

8. Physical-World Attacks

9. Evaluating Model Robustness to Adversarial Inputs

10. Defending Against Adversarial Inputs

11. Future Trends: Toward Robust AI

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