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首页> 外文期刊>Journal of electronic imaging >Secure image transmission based on visual cryptography scheme and artificial neural network-particle swarm optimization-guided adaptive vector quantization
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Secure image transmission based on visual cryptography scheme and artificial neural network-particle swarm optimization-guided adaptive vector quantization

机译：基于视觉加密方案的安全图像传输和人工神经网络粒子群优化引导的自适应矢量量化

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摘要

Image transmission holds a major share in data communication, and thus secure image transmission is currently a challenging domain of research. A secure image transmission scheme is proposed that physically transmits the encrypted image employing visual cryptography scheme (VCS). During physical transmission, the meaningless shares may attract curious hackers and if captured and stacked, the secret may be revealed. Moreover, the increase in transmission overhead due to multiple share images resulted from a single secret image after encryption is another concern regarding the physical implementation of VCS. Focusing on both observations, vector quantization (VQ) is used to encode as well as to compress each of the shares before transmission. To utilize VQ, its two parameters, cell width and dimension of grid, are needed to be optimized for various kind of images without compromising the randomness property of the shares. Hence, a particle swarm optimization-guided VQ is proposed, and furthermore, a multilayer perceptron in conjunction with an autoencoder are also trained in synchronism with that to automatically obtain the optimal VQ for each image type during the transmission. The proposed scheme is successfully implemented with different types of images for secure physical transmission with a 62.8% data volume reduction and 98.07% image quality retrieval. (C) 2019 SPIE and IS&T
机译：图像传输在数据通信中持有主要共享，因此安全图像传输当前是一个具有挑战性的研究领域。提出了一种安全图像传输方案，其物理地发送采用视觉加密方案（VCS）的加密图像。在物理传播中，无意义的股票可能会吸引好奇的黑客，如果捕获和堆叠，可能会揭示秘密。此外，由于在加密之后，由于多个秘密图像导致的传输开销的增加是关于VCS的物理实现的另一个问题。专注于两者观察，矢量量化（VQ）用于编码以及在传输之前压缩每个股票。为了利用VQ，需要为各种图像进行优化，而不需要损害股份的随机性属性，因此需要对网格的两个参数，单元宽度和尺寸进行优化。因此，提出了一种粒子群优化导向的VQ，并且还提出了与AutoEncoder结合的多层Perceptron，其同步训练，以便在传输期间自动获得每个图像类型的最佳VQ。所提出的方案以不同类型的图像成功实施，以确保具有62.8％的数据量减少和98.07％的图像质量检索。 （c）2019 SPIE和IS＆T

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