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

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldnt trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNsthe algorithms intrinsic to much of AIare used daily to process image, audioand video data.Author Katy Warr considers attack motivations, the risks posed this adversarial inputand methods for increasing AI robustness to these attacks. If youre a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attackor someone fascinated the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked 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