Your browser doesn't support javascript.
loading
Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis.
Pan, I-Hui; Huang, Ping-Sheng; Chang, Te-Jen; Chen, Hsiang-Hsiung.
Afiliação
  • Pan IH; Air Command and Staff College, National Defense University, Taoyuan 335, Taiwan.
  • Huang PS; Department of Electronic Engineering, Ming Chuan University, Taoyuan 333, Taiwan.
  • Chang TJ; Department of Electrical and Electronic Engineering, Chung-Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan.
  • Chen HH; Department of Electrical and Electronic Engineering, Chung-Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan.
Sensors (Basel) ; 22(13)2022 Jun 28.
Article em En | MEDLINE | ID: mdl-35808367
ABSTRACT
The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan