Strengthening Deep Neural Networks

Chapter 1. Introduction

This book is concerned with deep neural networks (DNNs), the deep learning algorithms that underpin many aspects of artificial intelligence (AI). AI covers the broad discipline of creating intelligent machines that mimic human intelligence capabilities such as the processing and interpretation of images, audio, and language; learning from and interacting with unpredictable physical and digital environments; and reasoning about abstract ideas and concepts. While AI also exploits other methods such as the broader field of machine learning (ML) and traditionally programmed algorithms, the ability of deep learning to imitate human capabilities places DNNs central to this discipline. DNNs can mimic, and often exceed, human capability in many tasks, such as image processing, speech recognition, and text comprehension. However, this book is not about how accurate or fast DNNs are; it’s about how they can be fooled and what can be done to strengthen them against such trickery.

This introduction will begin with a brief explanation of DNNs, including some history and when it first became apparent that they might not always return the answer that we expect. This introductory chapter then goes on to explain what comprises adversarial input and its potential implications in a society where AI is becoming increasingly prevalent.

A Shallow Introduction to Deep Learning

A DNN is a type of machine learning algorithm. In contrast to traditional software programs, these ...