9789819704279 - attacks, defenses and testing for deep learning di chen, jinyin; zhang, ximin; zheng, haibin (8 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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EUR 204,85
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Taschenbuch. Condizione: Neu. Attacks, Defenses and Testing for Deep Learning | Jinyin Chen (u. a.) | Taschenbuch | xx | Englisch | 2025 | Springer | EAN 9789819704279 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Da: Books Puddle, New York, U.S.A.Books Puddle
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EUR 303,06
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Condizione: New.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 244,86
EUR 63,17 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering.
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Da: Brook Bookstore On Demand, Napoli, ItaliaBrook Bookstore On Demand
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EUR 182,29
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Condizione: new. Questo è un articolo print on demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 235,39
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 420 pp. Englisch.
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Da: buchversandmimpf2000, Emtmannsberg, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a systematic study on the security of deep learning. With its powerful learning ability, deep learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers h…ave revealed that deep learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take autonomous driving as an example, there were more than 12 serious autonomous driving accidents in the world in 2018, including Uber, Tesla and other high technological enterprises. Drawing on the reviewed literature, we need to discover vulnerabilities in deep learning through attacks, reinforce its defense, and test model performance to ensure its robustness.Attacks can be divided into adversarial attacks and poisoning attacks. Adversarial attacks occur during the model testing phase, where the attacker obtains adversarial examples by adding small perturbations. Poisoning attacks occur during the model training phase, wherethe attacker injects poisoned examples into the training dataset, embedding a backdoor trigger in the trained deep learning model.An effective defense method is an important guarantee for the application of deep learning. The existing defense methods are divided into three types, including the data modification defense method, model modification defense method, and network add-on method. The data modification defense method performs adversarial defense by fine-tuning the input data. The model modification defense method adjusts the model framework to achieve the effect of defending against attacks. The network add-on method prevents the adversarial examples by training the adversarial example detector.Testing deep neural networks is an effective method to measure the security and robustness of deep learning models. Through test evaluation, security vulnerabilities and weaknesses in deep neural networks can be identified. By identifying and fixing these vulnerabilities, the security and robustness of the model can be improved.Our audience includes researchers in the field of deep learning security, as well as software development engineers specializing in deep learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 420 pp. Englisch.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 320,67
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Condizione: New. Print on Demand.
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Da: Biblios, frankfurt am main, GermaniaBiblios
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EUR 319,78
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND.
