An image encryption scheme based on multi-objective optimization and block compressed sensing

Visually meaningful image encryption may keep the data security and appearance security of the digital images. However, there are still security and efficiency shortcomings existing in the current algorithms. To solve these problems, we propose an effective visually meaningful color image encryption scheme by combining hybrid multi-objective particle swarm optimization (HMPSO), block compressed sensing (BCS) and Hessenberg decomposition (HD). Firstly, the R, G, B components of color image are segmented averagely and represented sparsely by discrete cosine transform (DCT), respectively. Next, the obtained sparse images are scrambled by the use of zigzag path and measured by BCS to obtain the measurement value matrices. To improve its security, the key associated with the plain image is used as the initial value of the nonlinear chaotic system Henon, and a cross-component dislocation and diffusion strategy are applied to the measurements using the chaotic sequences generated by Henon to obtain the secret image, which enhances the ability of the algorithm to resist chosen-plaintext attack. Subsequently, the secret images are fused into the carrier image by the HD embedding algorithm to generate the final visually meaningful cipher image. In addition, in order to enhance the quality of the reconstructed image and cipher image, HMPSO is implemented to optimize the threshold value of sparse coefficient modification and the embedding rate simultaneously. Simulation results and performance analysis demonstrate the effectiveness, confidentiality and robustness of the proposed scheme.