Key generation for static visual watermarking by machine learning

Kensuke Naoe, Hideyasu Sasaki, Yoshiyasu Takefuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digital watermarking became a key technology for protecting copyrights. In this paper, we propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns as training data set for machine learning of digital watermark Second, the proposed method of watermark extraction is processed by presenting visual features by the training approach of machine learning technology. Third, the training approach is to converge the extraction key as the classifier, which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Pages3089-3094
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 10
Event2009 International Conference on Machine Learning and Cybernetics - Baoding, China
Duration: 2009 Jul 122009 Jul 15

Publication series

NameProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume5

Other

Other2009 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityBaoding
Period09/7/1209/7/15

Keywords

  • Copyright protection
  • Digital watermarking
  • Key generation
  • Machine learning
  • Neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering

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