Adversarial AI Threat Response and Secure by Goran Trajkovski (.PDF)+
File Size: 48.9 MB
Adversarial AI Threat Response and Secure Model Design: Practical Techniques for Detecting, Preventing, and Managing AI Vulnerabilities by Goran Trajkovski
Requirements: .PDF, .EPUB reader, 48.9 MB | True PDF, True EPUB
Overview: 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 against, and designing resilient Machine Learning systems in the face of growing adversarial threats. Written by a leading expert in AI security and policy, this book delivers a combination of technical depth, practical implementation, and strategic insight. It begins by mapping the full landscape of adversarial threats—evasion, poisoning, model extraction, backdoors, and more—across diverse data modalities and real-world applications. From there, it equips readers with a robust toolkit of detection and defense techniques, including adversarial training, anomaly detection, and formal robustness certification. Ideal readers have working knowledge of Machine Learning fundamentals, programming experience in Python, and familiarity with IT environments. Nevertheless, deep mathematical background or prior adversarial AI experience is not required. Each unit builds systematically on previous knowledge while providing sufficient context for readers from diverse technical backgrounds. Supplementary materials and companion code help bridge knowledge gaps as you progress. If you can train a basic neural network, read Python code, and understand gradient descent conceptually, you have sufficient background to succeed with this material. Written for technical professionals and researchers who are building, deploying, or securing Machine Learning systems in real-world environments. The primary audience includes Machine Learning engineers, AI developers, cybersecurity professionals, and graduate-level students in Computer Science, Data Science, and applied AI programs.
Genre: Non-Fiction > Tech & Devices
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