Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery 1st Edition

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 зачарований by the differences between artificial and biological perception, this book is for you.