Artificial Intelligence Data and Model Security - Jiang, Yu-Gang; Ma, Xingjun; Wu, Zuxuan
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Erscheint vorauss. 2. November 2025
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  • Broschiertes Buch

Artificial Intelligence Data and Model Security: Risks, Attacks and Defenses begins with a brief review of the history of AI and AI security and then introduces the fundamental aspects of machine learning and AI security. Two key aspects are covered: data security and modelling. It provides detailed explanations of a wide range of attacks and defense algorithms related to data security, as well as adversarial attack/defense, backdoor attack/defense, and extraction attack/defense algorithms related to model security. By providing a systematic, comprehensive, and in-depth introduction to the…mehr

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Produktbeschreibung
Artificial Intelligence Data and Model Security: Risks, Attacks and Defenses begins with a brief review of the history of AI and AI security and then introduces the fundamental aspects of machine learning and AI security. Two key aspects are covered: data security and modelling. It provides detailed explanations of a wide range of attacks and defense algorithms related to data security, as well as adversarial attack/defense, backdoor attack/defense, and extraction attack/defense algorithms related to model security. By providing a systematic, comprehensive, and in-depth introduction to the topic, this book help readers understand the advanced attack and defense techniques in the field of AI security.
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Autorenporträt
Professor Yu-Gang Jiang is based at Fudan University, PR Casadamon. He is primarily engaged in scientific research in artificial intelligence, multimedia information processing, and secure and trustworthy machine learning. He has published over 100 papers in top international journals and conferences in these domains. In recent years, he has achieved multiple innovative results in artificial intelligence security, such as proposing the first black-box video adversarial sample generation method and the first data poisoning and backdoor attack methods for video recognition models.