先进的彩色隐蔽图像共享使用阿诺德猫地图和视觉密码学

B. Sapna, K. Sudha
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引用次数: 0

摘要

随着互联网的不断发展,对有效信息安全方案的需求与日俱增。可视化密码学(VC)是一种非常重要的秘密共享方案。这种秘密共享方案背后的关键步骤是将彩色隐蔽图像转换为多个不可加密的图像共享,因此除非通过一些数学计算进行组合,否则它无法揭示彩色隐蔽图像内的数据。本文提出了一种利用Arnold-cat映射(ACM)和VC的高级彩色隐蔽图像共享方案。随机矩阵编码方案将彩色隐蔽图像编码为图像矩阵。ACM算法破坏图像矩阵的像素之间的高相关性以生成加密图像。这个加密图像的共享是由VC生成的,它使用像素反转和随机矩阵生成器。一个接一个的共享不提供任何关于彩色隐蔽图像的信息,但是它们组合在一起提供了加密图像。投影范式提供了三个级别的安全性,并通过解密在不丢失信息的情况下返回秘密图像。相关实例和实验结果表明了该方案的有效性。
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ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY
The demand for effective information security schemes is increasing day by day with the continual growth of the internet. Visual cryptography (VC) is a very important secret sharing scheme. The essential step behind this secret sharing scheme is to convert the color covert image into multiple indecipherable image shares so it cannot reveal the data within the color covert image unless combined along by some mathematical calculation. This paper proposes an advanced color covert image-sharing scheme using Arnold cat map (ACM) and VC. The random matrix-encoding scheme encodes the color covert image into an image matrix. ACM algorithm disrupts the high correlation among the pixels of the image matrix to generate an encrypted image. The generation of shares from this encrypted image is by VC that uses pixel reversal and random matrix generator. The shares one by one does not provide any information concerning the color covert image however put together they offer back the encrypted image. The projected paradigm offers 3 levels of security and through decipherment gives back the covert image without loss of information. Related examples and experimental results reveal the effectiveness of this scheme.
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期刊最新文献
DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM SMART GESTURE USING REAL TIME OBJECT TRACKING CLASSIFICATION OF BRAIN TUMOR USING BEES SWARM OPTIMISATION
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