Name: Artificial Intelligence. Data and Model Safety
Price: 7557.00 TWD
Availability: InStock
Author: Xingjun, PhD Ma;Zuxuan, PhD Wu;Yu-Gang, PhD Jiang
ISBN: 9780443248405

portada Artificial Intelligence. Data and Model Safety

Artificial Intelligence. Data and Model Safety

Type

Physical Book

Publisher

Elsevier - Health Sciences Division

Author

Xingjun, PhD Ma;Zuxuan, PhD Wu;Yu-Gang, PhD Jiang

Year

2025

Pages

386

Format

Paperback

Dimensions

22.90 x 15.20 cm

ISBN13

9780443248405

Artificial Intelligence. Data and Model Safety

Xingjun, Phd Ma;Zuxuan, Phd Wu;Yu-Gang, Phd Jiang (Author) · Elsevier - Health Sciences Division · Paperback

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Synopsis "Artificial Intelligence. Data and Model Safety"

Artificial Intelligence Data and Model Safety: Risks, Attacks and Defenses offers a comprehensive overview of the evolution of AI and its security concerns. The book delves into how historical advancements in AI have both bolstered and complicated the issue of safeguarding data and models. By reflecting on the interplay between machine learning innovations and vulnerabilities, it sets the stage for readers to understand the critical importance of robust defenses in this era of digital and algorithmic reliance. In addition to contextualizing the historical trajectory of AI security, the book examines foundational elements of machine learning, emphasizing the mechanisms that contribute to, or mitigate, risks. Readers are guided through case studies of real-world attacks, illustrating the practical implications of security weaknesses, while proposed defense strategies provide actionable insights for strengthening AI systems.

Artificial Intelligence Data and Model Safety: Risks, Attacks and Defenses offers a comprehensive overview of the evolution of AI and its security concerns. The book delves into how historical advancements in AI have both bolstered and complicated the issue of safeguarding data and models. By reflecting on the interplay between machine learning innovations and vulnerabilities, it sets the stage for readers to understand the critical importance of robust defenses in this era of digital and algorithmic reliance. In addition to contextualizing the historical trajectory of AI security, the book examines foundational elements of machine learning, emphasizing the mechanisms that contribute to, or mitigate, risks.

Readers are guided through case studies of real-world attacks, illustrating the practical implications of security weaknesses, while proposed defense strategies provide actionable insights for strengthening AI systems.