Strengthening Deep Neural Networks - Warr, Katy
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As Deep Neural Networks (DNNs) become increasingly common in real-world applications, the potential to "fool" them presents a new attack vector. Warr examines the motivations attackers have for exploiting flaws in DNN algorithms and how to assess the threat to systems incorporating neural network technology.

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Produktbeschreibung
As Deep Neural Networks (DNNs) become increasingly common in real-world applications, the potential to "fool" them presents a new attack vector. Warr examines the motivations attackers have for exploiting flaws in DNN algorithms and how to assess the threat to systems incorporating neural network technology.
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Autorenporträt
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.