£114.57

Springer - Adversarial Machine Learning Book

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Description

Explore the complexities of modern artificial intelligence with Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence. Published by Springer, this academic resource provides a deep look into the vulnerabilities and security protocols of AI systems. This book serves as a specialized guide for professionals and students navigating the field of computer science. It covers the essential landscape of attack surfaces and the various defense mechanisms required to protect machine learning models. By studying the learning theories behind these interactions, readers can better understand how to build more resilient AI architectures. Whether you are researching cybersecurity or developing advanced machine learning models, this text offers the theoretical foundation needed to address modern AI threats. Secure your understanding of artificial intelligence security with this comprehensive work from Springer.

Key Features

Comprehensive coverage of attack surfaces within artificial intelligence systems.

Detailed analysis of defense mechanisms used to protect machine learning models.

In-depth exploration of learning theories relevant to adversarial machine learning.

Academic quality publication from the reputable brand Springer.

Essential reading for computer science and AI research professionals.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 March 2024
Listed Since
10 February 2024

Barcode

No barcode data available

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