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warr k strengthening deep neural networks trickery 2019

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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

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warr k strengthening deep neural networks trickery 2019