首页|Verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL)

Verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL)

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© 2022 Elsevier GmbH(objective) Image encryption converts the plain image into a noise-like one to protect information security. However, the obtained cipher images are easy to attract the attention of third parties during transmission and storage, and it is vulnerable to information damage and tampering caused by hacking or noise pollution, which in turn makes the receiver unable to obtain the correct decrypted image even spending many resources. To address these issues, this paper proposed a verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL). (methods) Specifically, the plain image is preprocessed and compressed to obtain compressed image via CS, achieving the purpose of reducing the image size. Subsequently, to improve the encryption effect, dynamic cyclic shift confusion method (DCSCM) and diffusion method based on improved game of life (DMIGOL) are presented and performed on the compressed image to obtain the secret image. Next, multiple shadow images are obtained using the secret image sharing based on the Chinese remainder theorem (CRT-SIS) on the secret image, which improves the robustness of this scheme and achieves multi-party data transfer of image. Finally, the shadow images are embedded into the carrier images respectively to obtain visually meaningful cipher images, reducing the attacker's attention to the cipher images during transmission. Additionally, the Hamming distance authentication method relying on plain image and carrier image is applied, and users can perform identity authentication and integrity check on cipher image. (result) Experimental results demonstrate the security and effectiveness of the proposed scheme.

Compressed sensing (CS)Game of life (GOL)Image encryptionSecret image sharing (SIS)

Chai X.、Fu Z.、Ma Y.、Gan Z.、Long G.、Zhou L.

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School of Artificial Intelligence Henan Key Laboratory of Big Data Analysis and Processing Henan University

School of Software Intelligent Data Processing Engineering Research Center of Henan Province Institute of Intelligent Network System Henan University

2022

10.1016/j.ijleo.2022.169375

Optik

Optik

EISCI
ISSN：0030-4026
年,卷(期)：2022.262
  • 42