Damageless image hashing using neural network

Kensuke Naoe, Yoshiyasu Takefuji

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we present a new key generation model for image hashing using neural network, which does not embed any data into the content but is able to extract meaningful data from target image. This model trains artificial neural network to assign predefined code and uses this trained artificial neural network weight and the coordinates of the selected feature sub blocks of target image as keys to extract the predefined code. In this model, the observed output signal from the trained neural network is used as image hash value which distinguishes the target image from other images. The proposed method contributes to secure image hashing for content identification without damaging or losing any detailed data of visual images. The proposed method realizes an application for image authentication, image similarity comparison, verification of image integrity and copyright protection of multimedia contents.

本文言語English
ホスト出版物のタイトルProceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010
ページ442-447
ページ数6
DOI
出版ステータスPublished - 2010 12 1
イベント2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010 - Cergy-Pontoise, France
継続期間: 2010 12 72010 12 10

出版物シリーズ

名前Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010

Other

Other2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010
CountryFrance
CityCergy-Pontoise
Period10/12/710/12/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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