Azala

Digital Watermarking for Machine Learning Model Techniques, Protocols and Applications /

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 i...

Deskribapen osoa

Gorde:
Xehetasun bibliografikoak
Erakunde egilea: SpringerLink (Online service)
Beste egile batzuk: Fan, Lixin (Argitaratzailea), Chan, Chee Seng (Argitaratzailea), Yang, Qiang (Argitaratzailea)
Formatua: Baliabide elektronikoa eBook
Hizkuntza:ingelesa
Argitaratua: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edizioa:1st ed. 2023.
Gaiak:
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.
Sarrera elektronikoa:https://doi.org/10.1007/978-981-19-7554-7
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Aurkibidea:
  • 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 Models
  • Chapter 11. Model Auditing For Data Intellectual Property .

Antzeko izenburuak