Your browser doesn't support javascript.
loading
A visually secure image encryption method based on integer wavelet transform and rhombus prediction.
Chen, Xianyi; Zou, Mengling; Yang, Bin; Wang, Zhenli; Wu, Nannan; Qi, Lili.
Afiliação
  • Chen X; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Zou M; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China.
  • Yang B; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Wang Z; School of Design, Jiangnan University, Wuxi 214028, China.
  • Wu N; Computer Information and Network Security Department, Jiangsu Police Institute, Nanjing 210031, China.
  • Qi L; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Math Biosci Eng ; 18(2): 1722-1739, 2021 Feb 07.
Article em En | MEDLINE | ID: mdl-33757207
ABSTRACT
Traditional image encryption technology usually encrypts a normal image into a noise matrix, which can protect the image in a certain extent, but noise appearance is easy to arouse the suspicion of attackers. To avoid this problem, a method of encrypting image into carrier image with visual meaning is proposed. Inspired by the existing visually secure encryption technique, we proposed an improved method based on the integer wavelet transform (IWT) and prediction scheme. The secret image is hidden in the high frequency coefficients of IWT to achieve good invisibility, and prediction error are used to replace the pixels of the carrier image to improve the final image quality. Experimental results and analysis show that the quality of the encrypted image is 3.5 dB better than that of the previous ones.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article