A fast pseudo-stochastic sequential cipher generator based on RBMs

Abstract

Based on Restricted Boltzmann machines, an improved pseudo-stochastic sequential cipher generator is proposed. It is effective and efficient because of the two advantages: this generator includes a stochastic neural network that can perform the calculation in parallel, that is to say, all elements are calculated simultaneously; unlimited number of sequential ciphers can be generated simultaneously for multiple encryption schemas. The periodicity and the correlation of the output sequential ciphers meet requirements for the design of encrypting sequential data. In the experiment, the generated sequential cipher is used to encrypt images, and better performance is achieved in terms of the key space analysis, the correlation analysis, the sensitivity analysis and the differential attack. To evaluate the efficiency of our method, a comparative study is performed with a prevalent method called “logistic map.” Our approach achieves a better performance on running time estimation. The experimental results are promising as the proposed method could promote the development of image protection in computer security.