A Fast Pseudo-Stochastic Sequential Cipher Generator Based on RBMs

TL;DR

This paper introduces a fast, parallelizable pseudo-stochastic cipher generator based on RBMs, enhancing image encryption security with improved key space, correlation, and sensitivity properties.

Contribution

It presents a novel RBM-based cipher generator capable of producing multiple secure sequential ciphers simultaneously, improving efficiency and security in image encryption.

Findings

Enhanced key space and correlation properties

Improved resistance to differential attacks

Effective encryption of images with promising results

Abstract

Based on Restricted Boltzmann Machines (RBMs), 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 the requirements for the design of encrypting sequential data. In the experiment, the generated sequential cipher is used to encrypt the image, and better performance is achieved in terms of the key space analysis, the correlation analysis, the sensitivity analysis and the differential attack. The experimental result is promising that could promote the development of image protection in computer security.