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Low Computational Color Secret Sharing Schemes with High Shares Quality Color Secret Sharing

机译:具有高共享质量的低计算色彩秘密共享方案

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Secret image sharing, a technology of secret communication, transmits secret images by distributing camouflage images (shares) to a group of participants, where each camouflage image contains a part of secret images. Secret images can be reconstructed under the conditions of a sufficient number of shares gathered and processed using a revealing algorithm. In 2009, Tsai et al. proposed a novel secret image sharing scheme using exclusive-or operators, principal component analysis (PCA), and feed-forward (FF) neural networks. The performance of their scheme was better than that of more recently proposed schemes, however, PCA and FF neural networks are time-consuming technologies and secret images cannot be completely restored. In this paper, we proposed an improved secret image sharing scheme to solve these problems. Experimental results show that camouflage image qualities of the proposed method were 54.26 dB on average, which is higher than that of Tsai et al.¡¦s scheme. Meanwhile, hiding capacity of the proposed mechanism was 1.125 times larger than that of Tsai et al.¡¦s scheme. Additionally, results also contribute to the literature by illustrating that original secret images can be reversed.
机译:秘密图像共享是一种秘密通信技术,它通过将伪装图像(共享)分发给一组参与者来传输秘密图像,其中每个伪装图像都包含一部分秘密图像。可以在使用显示算法收集和处理足够数量的份额的条件下重建秘密图像。 2009年,蔡等人。提出了一种使用异或运算符,主成分分析(PCA)和前馈(FF)神经网络的新颖秘密图像共享方案。他们的方案的性能优于最近提出的方案,但是,PCA和FF神经网络是耗时的技术,秘密图像无法完全恢复。在本文中,我们提出了一种改进的秘密图像共享方案来解决这些问题。实验结果表明,该方法的伪装图像质量平均为54.26 dB,高于Tsai等人的方案。同时,所提机制的隐藏能力是Tsai等人方案的隐藏能力的1.125倍。另外,通过说明原始秘密图像可以颠倒,结果也有助于文献研究。

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