The rising tide of threats, from financial cybercrime to asymmetric military conflicts, demands greater sophistication in tools and techniques of law enforcement, commercial and domestic security professionals, and terrorism prevention. Concentrating on computational solutions to determine or anticipate an adversary's intent, Adversarial Reasoning:

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Andrew Bailey’s highly-regarded introductory anthology has been revised and updated in this new concise edition. Mindful of the intrinsic difficulty of the material, the editors provide comprehensive introductions both to each topic and to each individual selection. By presenting a detailed discussion of the historical and intellectual background to each piece, the editors enable readers to approach the material without unnecessary barriers to understanding. Helpful explanatory footnotes are provided throughout, and new sections on philosophical puzzles and paradoxes and philosophical terminology have been added.

Despite an overwhelming scientific consensus, climate change remains one of the most controversial issues of our time. Focusing on the rhetoric that surrounds the issue of climate change, this groundbreaking book analyses why the debate continues to rage and examines how we should argue when winning the argument really matters. Going beyond routine condemnations of the wildest statements made by religious fundamentalists or spokespeople for fossil fuel interests, the book explains the mutually exacerbating problems that permit many of us greet catastrophic predictions with an equivocal shrug. It argues that the argumentative situation around climate change makes a certain kind of skepticism – "fair-minded skepticism" – not only possible but likely. The book also strikes a hopeful note, reminding us that people do change their minds in response to effective argumentation that appeals to deeply shared values. Offering new insight into an ongoing academic discussion about the nature of argument and how it can be undertaken more effectively and ethically, as well as a new perspective on the rhetoric of science and technology, this book will be a valuable resource to students and scholars of climate change, environmental humanities, rhetoric, environmental communication, sociology and science and technology studies.

This book reports on the latest research and developments in the field of human factors in cybersecurity. It analyzes how the human vulnerabilities can be exploited by cybercriminals and proposes methods and tools to increase cybersecurity awareness. The chapters cover the social, economic and behavioral aspects of the cyberspace, providing a comprehensive perspective to manage cybersecurity risks. By gathering the proceedings of the AHFE Virtual Conference on Human Factors Cybersecurity, held on July 16–20, 2020, this book offers a timely perspective of key psychological and organizational factors influencing cybersecurity, reporting on technical tools, training methods and personnel management strategies that should enable achieving a holistic cyber protection for both individuals and organizations. By combining concepts and methods of engineering, education, computer science and psychology, it offers an inspiring guide for researchers and professionals, as well as decision-makers, working at the interfaces of those fields.

This book constitutes selected papers presented at the First International Workshop on Deceptive AI, DeceptECAI 2020, held in conjunction with the 24th European Conference on Artificial Intelligence, ECAI 2020, in Santiago de Compostela, Spain, in August 2020, and Second International Workshop on Deceptive AI, DeceptAI 2021, held in conjunction with the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, in Montreal, Canada, in August 2021. Due to the COVID-19 pandemic both conferences were held in a virtual mode. The 12 papers presented were thoroughly reviewed and selected from the 16 submissions. They present recent developments in the growing area of research in the interface between deception and AI.

This book constitutes the refereed proceedings of the 29th International Symposium on Static Analysis, SAS 2022, held in Auckland, New Zealand, in December 2022. The 18 full papers included in this book were carefully reviewed and selected from 43 submissions. Static analysis is widely recognized as a fundamental tool for program verification, bug detection, compiler optimization, program understanding, and software maintenance. The papers deal with theoretical, practical and application advances in the area.