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Publicado por now publishers Inc, Hanover, 2024
ISBN 10: 1638283400ISBN 13: 9781638283409
Librería: Grand Eagle Retail, Wilmington, DE, Estados Unidos de America
Libro
Paperback. Condición: new. Paperback. This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided. In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Now Publishers, 2024
ISBN 10: 1638283400ISBN 13: 9781638283409
Librería: California Books, Miami, FL, Estados Unidos de America
Libro
Condición: New.
Publicado por Now Publishers Inc, 2024
ISBN 10: 1638283400ISBN 13: 9781638283409
Librería: Revaluation Books, Exeter, Reino Unido
Libro
Paperback. Condición: Brand New. 112 pages. 9.09x6.10x0.32 inches. In Stock.
Publicado por now publishers Inc, Hanover, 2024
ISBN 10: 1638283400ISBN 13: 9781638283409
Librería: AussieBookSeller, Truganina, VIC, Australia
Libro
Paperback. Condición: new. Paperback. This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided. In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por Now Publishers Inc, 2024
ISBN 10: 1638283400ISBN 13: 9781638283409
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro Impresión bajo demanda
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided.