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Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery by Katy Warr PDF

as deep neural networks (dnns) become increasingly common in real-world applications, The potential to deliberately "fool" Them with data that couldn'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.
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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 DNS and discover how they could be tricked by adversarial input
investigate methods used to generate adversarial input capable of fooling dnns
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

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