What do you think?
Rate this book
Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery
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.
244 pages, Paperback
Published September 3, 2019
3 people are currently reading
37 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.
Can't find what you're looking for?
Get help and learn more about the design.