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1. |
Record Nr. |
UNINA9910970611903321 |
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Autore |
White Miles <1954-> |
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Titolo |
From Jim Crow to Jay-Z : race, rap, and the performance of masculinity / / by Miles White |
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Pubbl/distr/stampa |
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Urbana, : University of Illinois Press, c2011 |
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ISBN |
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9786613432001 |
9781283432009 |
1283432005 |
9780252093678 |
0252093674 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (177 p.) |
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Collana |
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African American music in global perspective |
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Disciplina |
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Soggetti |
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Rap (Music) - Social aspects - United States |
African American men - Race identity |
Music and race |
Masculinity - United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Shadow and act : American popular music and the absent black presence -- The fire this time : black masculinity and the politics of racial performance -- Affective gestures : hip-hop aesthetics, blackness and the literacy of performance -- Real niggas : black men, hard men, and the rise of gangsta culture -- Race rebels : whiteness and the new masculine desire. |
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Sommario/riassunto |
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This multilayered study of the representation of black masculinity in musical and cultural performance takes aim at the reduction of African American male culture to stereotypes of deviance, misogyny, and excess. |
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2. |
Record Nr. |
UNINA9910728383303321 |
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Autore |
Fan Lixin |
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Titolo |
Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (233 pages) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Machine learning |
Data protection |
Image processing - Digital techniques |
Computer vision |
Image processing |
Machine Learning |
Data and Information Security |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Image Processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network |
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Models -- Chapter 11. Model Auditing For Data Intellectual Property . |
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Sommario/riassunto |
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Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings. |
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