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.
1. 1: AI and AI safety: an introduction - p. 1 2. 2: Machine learning basics - p. 15 3. 3: AI safety basics - p. 51 4. 4: Data safety: attacks - p. 77 5. 5: Data safety: defenses - p. 125 6. 6: Model safety: adversarial attacks - p. 163 7. 7: Model safety: adversarial defenses - p. 191 8. 8: Model safety: backdoor attacks - p. 251 9. 9: Model safety: backdoor defenses - p. 275 10. 10: Model safety: extraction attacks and defenses - p. 295 11. 11: Future prospects - p. 319 12. Bibliography - p. 325 13. Index - p. 355
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.
1. 1: AI and AI safety: an introduction - p. 1 2. 2: Machine learning basics - p. 15 3. 3: AI safety basics - p. 51 4. 4: Data safety: attacks - p. 77 5. 5: Data safety: defenses - p. 125 6. 6: Model safety: adversarial attacks - p. 163 7. 7: Model safety: adversarial defenses - p. 191 8. 8: Model safety: backdoor attacks - p. 251 9. 9: Model safety: backdoor defenses - p. 275 10. 10: Model safety: extraction attacks and defenses - p. 295 11. 11: Future prospects - p. 319 12. Bibliography - p. 325 13. Index - p. 355
