Read Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Katy Warr

4.9/5 (11870 ratings)

Description: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 inputInvestigate methods used to generate adversarial input capable of fooling DNNsExplore real-world scenarios and model the adversarial threatEvaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial dataExamine the potential future of AI to see how it might become better at mimicking human perception in years to comeWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery. To get started finding Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery, you are right to find our website which has a comprehensive collection of manuals listed.
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ISBN: 149204492X

Description: 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 inputInvestigate methods used to generate adversarial input capable of fooling DNNsExplore real-world scenarios and model the adversarial threatEvaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial dataExamine the potential future of AI to see how it might become better at mimicking human perception in years to comeWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery. To get started finding Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

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