Meaningful Secret Image Sharing with Uniform Image Quality

Abstract

In meaningful secret image sharing (MSIS), a secret image is divided into n shadows. Each shadow is meaningful and similar to the corresponding cover image. Meaningful shadows can reduce the suspicion of attackers in transmission and facilitate shadow management. Previous MSIS schemes always include pixel expansion, and cross-interference from different shadows may exist when cover images are extremely unnatural images with large black and white blocks. In this article, we propose an MSIS with uniform image quality. A threshold t is set to determine the absolute salient regions. More identical bits are allocated according to saliency values in the absolute saliency region, which can improve image quality. In addition, the new identical bits allocation strategy also adjusts the randomness of the shadow images, generating shadows with uniform image quality and avoiding the cross-interference between different shadows. Experimental results show the effectiveness of our proposed scheme.

Threshold Preview-able Essential Secret Image Sharing

“…A bitwise XOR operation is conducted on the essential shadows E₁, E₂, …, Eₜ, resulting in the generation of the cover shadow (CS). (6). A bitwise XOR operation is executed between the cover shadow (CS) and each of the initial shadows ISt₊₁, IS₂, …, ISₙ, yielding the n − t non-essential shadows NE₁, NE₂, …, NEₙ₋ₜ, respectively.…”

“…Building upon the framework established by Thien and Lin [3], Chen and Lin [4] introduced a progressive secret image transmission method rooted in secret image sharing. To counteract potential security threats posed by hackers, they devised secret image sharing mechanisms incorporating meaningful or disguised shares were designed [5,6]. Additionally, various research endeavors within the realm of secret image sharing [7][8][9][10][11][12][13][14][15][16][17][18][19][20] include verifiable secret image sharing schemes [7,8], essential secret image sharing methodologies [9][10][11][12][13][14][15][16], among others.…”

A Derivative Fidelity-Based Total Generalized Variation Method for Image Restoration

“…Degraded images may affect the accuracy of image detection and image classification [1,2]. Image restoration obtains original clean images from observed ones according to the following image degradation module:…”