{"product_id":"9783032245212","title":"Trojan Code: Adversarial Machine Learning and Secure AI Systems","description":"\u003ch1\u003eTrojan Code: Adversarial Machine Learning and Secure AI Systems\u003c\/h1\u003e \u003ch2\u003eKallas, Kassem\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp style=\"text-align: justify;\"\u003e\u003cem\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; color: black;\"\u003eThis book\u003c\/span\u003e\u003c\/em\u003e\u003cspan class=\"apple-converted-space\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; color: black;\"\u003e \u003c\/span\u003e\u003c\/span\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; color: black;\"\u003eprovides a comprehensive and accessible guide to the rapidly growing field of AI security, addressing the threats, vulnerabilities, and defensive strategies that shape modern machine-learning systems. The book examines how adversaries exploit poisoned data, hidden triggers, model theft, and privacy leakage to compromise AI, and explains why securing learning systems requires approaches fundamentally different from traditional cybersecurity. Across four structured parts, it maps the threat landscape, dissects backdoor attacks, develops defensive and game-theoretic frameworks, and introduces robust watermarking methods for protecting AI intellectual property.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"text-align: justify;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; color: black;\"\u003eDrawing from real-world case studies in healthcare, finance, autonomous systems, and defense, the book translates academic research into practical insights for evaluating risk, designing resilient models, and understanding the economic and operational impact of AI breaches. Its coverage extends from adversarial examples and federated learning sabotage to ownership verification and governance-aware design.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"text-align: justify;\"\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: black; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;\"\u003eDesigned for researchers, engineers, graduate students, and institutional decision-makers,\u003cspan class=\"apple-converted-space\"\u003e this book\u003c\/span\u003e\u003cspan class=\"apple-converted-space\"\u003e \u003c\/span\u003eserves both as a technical reference and a strategic resource for organizations deploying AI in mission-critical environments. It equips readers with the knowledge needed to anticipate emerging threats and to build AI systems that are not only powerful and efficient, but secure, trustworthy, and resilient by design.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-08-10\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783032245212\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 396\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":46803329024140,"sku":"9783032245212","price":179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032245212.jpg?v=1779973668","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032245212","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}