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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

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

Warr, Katy

Paperback

ApplicationsGeneral Computers

Currently unavailable to order

ISBN10: 1492044954
ISBN13: 9781492044956
Publisher: Oreilly Media
Published: Sep 3 2019
Pages: 244
Weight: 0.90
Height: 0.50 Width: 7.00 Depth: 9.10
Language: English

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately fool them with data that wouldnâ t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNsâ the algorithms intrinsic to much of AIâ 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 youâ 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 input
  • Investigate methods used to generate adversarial input capable of fooling DNNs
  • Explore real-world scenarios and model the adversarial threat
  • 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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