Disinformation Security for Generative AI Applications, Gebunden

Klappentext

This book is an essential roadmap for AI researchers, security analysts, and policymakers, delivering actionable, cross-cutting frameworks to defend democratic institutions and digital ecosystems against the growing threat of AI-driven disinformation.

Generative artificial intelligence (AI) has ushered in a new era of content creation, enabling unprecedented capabilities in text, image, and video synthesis. However, these advancements come with significant risks, particularly the potential for AI to be exploited for disinformation. From deepfakes and synthetic text to AI-powered social engineering and adversarial attacks, malicious uses of generative AI pose serious threats to individuals, institutions, and democratic societies. Unlike traditional approaches that treat disinformation as a media or policy problem, this book frames it as a complex socio-technical security issue. It presents a layered perspective on disinformation threats, integrating advances in generative AI with detection strategies, policy frameworks, and ethical considerations. From adversarial machine learning and provenance tracking to explainable AI and decentralized content authentication, the book introduces cross-cutting methodologies to secure digital ecosystems against manipulation at scale.

At the heart of the book is the recognition that generative AI is both a tool and a threat. It can democratize creativity and intelligence, but without strong governance, it can just as easily erode truth, exploit biases, and amplify social discord. This duality is addressed through real-world use cases spanning sectors like journalism, healthcare, energy systems, and finance, where disinformation poses not just reputational risks but systemic vulnerabilities. The book highlights how technical innovation must be accompanied by transparency protocols, regulatory mechanisms, and ethical design to build societal resilience.

Readers will find the book:

• Bridges theory and practice by combining foundational concepts with real-world case studies across healthcare, media, engineering, and energy;
• Covers the full threat landscape of generative AI, including deepfakes, synthetic text, adversarial attacks, and AI-powered social engineering;
• Presents actionable methodologies for detection, mitigation, watermarking, and provenance tracking of AI-generated content;
• Addresses ethical, regulatory, and governance frameworks, making it ideal for interdisciplinary audiences.

Audience

AI researchers, machine learning engineers, cybersecurity professionals, data scientists, academics, and policymakers working on generative models, adversarial robustness, AI-driven threats, synthetic media, and digital forensics.