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This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addres...
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 th...
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative...
This book provides a comprehensive and accessible guide to the rapidly growing field of AI security, addressing the threats, vulnerabilities, and defensive strategies that shape modern machine-learning systems. The book examines how adversaries exploit poisoned data, hidden triggers, model thef...
The author provides a rigorous, technically grounded framework for analysing, modelling, and mitigating adversarial threats against artificial intelligence systems. The book focuses on adversarial machine learning and AI-native cyber-attacks, examining how threat actors exploit vulnerabilities in da...
From advanced post-exploitation techniques and lateral movement to stealthy persistence leveraging cloud-native misconfigurations, this book captures the methods actually employed by red teams and sophisticated adversaries in the wild. Readers will gain insight into weaponizing trust boundaries, exp...
From advanced post-exploitation techniques and lateral movement to stealthy persistence leveraging cloud-native misconfigurations, this book captures the methods actually employed by red teams and sophisticated adversaries in the wild. Readers will gain insight into weaponizing trust boundaries, exp...
From advanced post-exploitation techniques and lateral movement to stealthy persistence leveraging cloud-native misconfigurations, this book captures the methods actually employed by red teams and sophisticated adversaries in the wild. Readers will gain insight into weaponizing trust boundaries, exp...
This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addres...
This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addres...
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 th...
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 th...
Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneou...
Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneou...
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative...
As artificial intelligence becomes embedded in everything from healthcare diagnostics to financial systems and autonomous vehicles, the stakes for AI security have never been higher. Adversarial AI Threat Response and Secure Model Design is your essential guide to understanding, defending ...
As artificial intelligence becomes embedded in everything from healthcare diagnostics to financial systems and autonomous vehicles, the stakes for AI security have never been higher. Adversarial AI Threat Response and Secure Model Design is your essential guide to understanding, defending ...
This book constitutes the refereed proceedings of the 10th International Conference on Information Theoretic Security, ICITS 2017, held in Hong Kong, China, in November/December 2017.The 12 full papers were carefully reviewed and selected from 42 submissions. They are organized around the following ...
The author provides a rigorous, technically grounded framework for analysing, modelling, and mitigating adversarial threats against artificial intelligence systems. The book focuses on adversarial machine learning and AI-native cyber-attacks, examining how threat actors exploit vulnerabilities in da...
This book constitutes the refereed proceedings of the 10th International Conference on Information Theoretic Security, ICITS 2017, held in Hong Kong, China, in November/December 2017.The 12 full papers were carefully reviewed and selected from 42 submissions. They are organized around the following ...