Dual embedding model: a new framework for visually meaningful image encryption

Abstract

Visually meaningful image encryption (VMIE) means that a plain image is transformed into a visually meaningful cipher image which makes the plain image more imperceptible than the noise-like cipher image generated by traditional image encryption algorithms. In essence, existing VMIE algorithms exploit the idea of information steganography, i.e., embedding a secret into a host image to generate a cipher image which is visually similar to the original host image. However, it is well known that steganalysis technique is a fatal threat to steganography. Therefore, the security of existing VMIE algorithms will be potentially threatened by steganalysis technique. To improve the security of VMIE algorithms, we propose a new VMIE framework with dual embedding model. In the new framework an additional embedding phase is added. More specifically, in the first embedding process, the pre-encrypted image is embedded into the reference image to generate a visually meaningful reference cipher image. In the second embedding process, the difference between the visually meaningful reference cipher image and the original reference image is calculated to obtain a deviation matrix. Then, the deviation matrix is used as the disguised information and then embedded into the disguised host image to obtain a disguised visually meaningful encrypted image. The reference image can be any image with specified size thus ensuring the security of the VMIE algorithm. To verify the validity of the proposed VMIE framework, an example algorithm is proposed. Simulation results and performance analyses show that the example algorithm has a high time efficiency, high robustness and security.