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Based on Restricted Boltzmann Machines (RBMs), an improved pseudo-stochasticsequential cipher generator is proposed. It is effective and efficient becauseof the two advantages: this generator includes a stochastic neural network thatcan perform the calculation in parallel, that is to say, all elements arecalculated simultaneously; unlimited number of sequential ciphers can begenerated simultaneously for multiple encryption schemas. The periodicity andthe correlation of the output sequential ciphers meet the requirements for thedesign of encrypting sequential data. In the experiment, the generatedsequential cipher is used to encrypt the image, and better performance isachieved in terms of the key space analysis, the correlation analysis, thesensitivity analysis and the differential attack. The experimental result ispromising that could promote the development of image protection in computersecurity.

A Fast Pseudo-Stochastic Sequential Cipher Generator Based on RBMs