Strengthening Deep Neural Networks

Preface

Artificial intelligence (AI) is prevalent in our lives. Every day, machines make sense of complex data: surveillance systems perform facial recognition, digital assistants comprehend spoken language, and autonomous vehicles and robots are able to navigate the messy and unconstrained physical world. AI not only competes with human capabilities in areas such as image, audio, and text processing, but often exceeds human accuracy and speed.

While we celebrate advancements in AI, deep neural networks (DNNs)—the algorithms intrinsic to much of AI—have recently been proven to be at risk from attack through seemingly benign inputs. It is possible to fool DNNs by making subtle alterations to input data that often either remain undetected or are overlooked if presented to a human. For example, alterations to images that are so small as to remain unnoticed by humans can cause DNNs to misinterpret the image content. As many AI systems take their input from external sources—voice recognition devices or social media upload, for example—this ability to be tricked by adversarial input opens a new, often intriguing, security threat. This book is about this threat, what it tells us about DNNs, and how we can subsequently make AI more resilient to attack.

By considering real-world scenarios where AI is exploited in our daily lives to process image, audio, and video data, this book considers the motivations, feasibility, and risks posed by adversarial input. It provides both intuitive and ...