Secret image sharing with distinct covers based on improved Cycling-XOR

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Visual Communication and Image Representation Pub Date : 2024-08-31 DOI:10.1016/j.jvcir.2024.104282
Jiang-Yi Lin , Ji-Hwei Horng , Chin-Chen Chang
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Abstract

Secret image sharing (SIS) is a technique used to distribute confidential data by dividing it into multiple image shadows. Most of the existing approaches or algorithms protect confidential data by encryption with secret keys. This paper proposes a novel SIS scheme without using any secret key. The secret images are first quantized and encrypted by self-encryption into noisy ones. Then, the encrypted images are mixed into secret shares by cross-encryption. The image shadows are generated by replacing the lower bit-planes of the cover images with the secret shares. In the extraction phase, the receiver can restore the quantized secret images by combinatorial operations of the extracted secret shares. Experimental results show that our method is able to deliver a large amount of data payload with a satisfactory cover image quality. Besides, the computational load is very low since the whole scheme is mostly based on cycling-XOR operations.

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基于改进型循环-XOR 的不同封面秘密图像共享
秘密图像共享(SIS)是一种通过将机密数据分割成多个图像阴影来分发机密数据的技术。现有的大多数方法或算法都是通过使用秘钥加密来保护机密数据的。本文提出了一种无需使用任何秘钥的新型 SIS 方案。秘密图像首先被量化,并通过自加密技术加密成噪声图像。然后,通过交叉加密将加密图像混合成秘密份额。用秘密份额替换封面图像的低位平面,生成图像阴影。在提取阶段,接收方可以通过对提取的秘密份额进行组合运算来还原量化的秘密图像。实验结果表明,我们的方法能够以令人满意的覆盖图像质量提供大量数据有效载荷。此外,由于整个方案主要基于循环-XOR 运算,因此计算负荷非常低。
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来源期刊
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation 工程技术-计算机:软件工程
CiteScore
5.40
自引率
11.50%
发文量
188
审稿时长
9.9 months
期刊介绍: The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.
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