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A fast decryption algorithm for BSS-based image encryption

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The image encryption based on blind source separation (BSS) takes advantage of the underdetermined BSS problem to encrypt multiple confidential images. Its security can be further improved if the number of images to be simultaneously encrypted increases. However, the BSS decryption speed will correspondingly decrease since the computational load of the BSS algorithms usually has nonlinear relation with the number of the source signals. To solve the problem, this paper presents a fast decryption algorithm based on adaptive noise cancellation by using the knowledge of the key images, which are used in the BSS-based method and available at the receiving side. As a result, the number of the source signals for the fast BSS decryption is decreased in half, and the decryption time is considerably reduced. Both computer simulations and performance analyses demonstrate the efficiency of the proposed method.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III
EditorsJun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin
PublisherSpringer Verlag
Pages318-325
Number of pages8
ISBN (Electronic)9783540344834
ISBN (Print)9783540344827
DOIs
Publication statusPublished - 11 May 2006
Externally publishedYes
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 28 May 20061 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3973 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/TerritoryChina
CityChengdu
Period28/05/061/06/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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