A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network.

Arnold transform chaotic pulse-coupled neural network chaotic sequence image encryption image scrambling

Journal

Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874

Informations de publication

Date de publication:
10 Aug 2022
Historique:
received: 29 06 2022
revised: 28 07 2022
accepted: 08 08 2022
entrez: 26 8 2022
pubmed: 27 8 2022
medline: 27 8 2022
Statut: epublish

Résumé

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping.

Identifiants

pubmed: 36010767
pii: e24081103
doi: 10.3390/e24081103
pmc: PMC9407545
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Xiangyu Deng
ID : National Natural Science Foundation of China (No. 61961037)
Organisme : Xiangyu Deng
ID : Industrial Support Plan of Education Department of Gansu Province (No. 2021CYZC-30)

Déclaration de conflit d'intérêts

The authors declare no conflict of interest.

Références

IEEE Trans Neural Netw. 1999;10(3):480-98
pubmed: 18252547
Entropy (Basel). 2021 Sep 02;23(9):
pubmed: 34573784
Entropy (Basel). 2022 Feb 08;24(2):
pubmed: 35205545
Entropy (Basel). 2022 Feb 17;24(2):
pubmed: 35205581

Auteurs

Jinhong Ye (J)

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.
Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China.

Xiangyu Deng (X)

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.
Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China.

Aijia Zhang (A)

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.
Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China.

Haiyue Yu (H)

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.
Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China.

Classifications MeSH