Strengthening Deep Neural Networks

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Strengthening Deep Neural Networks

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內容描述

As Deep Neural Networks (DNNs) become increasingly common in real-world applications, the potential to "fool" them presents a new attack vector. In this book, author Katy Warr examines the security implications of how DNNs interpret audio and images very differently to humans.
You'll learn about the motivations attackers have for exploiting flaws in DNN algorithms and how to assess the threat to systems incorporating neural network technology. Through practical code examples, this book shows you how DNNs can be fooled and demonstrates the ways they can be hardened against trickery.

Learn the basic principles of how DNNs "think" and why this differs from our human understanding of the world
Understand adversarial motivations for fooling DNNs and the threat posed to real-world systems
Explore approaches for making software systems that incorporate DNNs less susceptible to trickery
Peer into the future of Artificial Neural Networks to learn how these algorithms may evolve to become more robust

作者介紹

Katy Warr works at Roke Manor Research in the UK creating solutions for complex real-world problems. She specializes in AI and data analytics and leads the company's technical strategy in these areas. Previously she worked at IBM UK Laboratories, architecting and developing software for a variety of distributed enterprise products with an emphasis on transactional integrity and security.

Katy gained her degree in AI and Computer Science from the University of Edinburgh at a time when there was insufficient compute power and data available for deep learning to be much more than a theoretical pursuit. Fast forward a few years and she considers herself fortunate to witness this exciting field becoming mainstream.