A Fast Pseudo-Stochastic Sequential Cipher Generator Based on RBMs
This work addresses image protection in computer security, but it is incremental as it builds on existing RBM-based methods.
The authors tackled the problem of generating pseudo-stochastic sequential ciphers for encryption by proposing an improved generator based on Restricted Boltzmann Machines (RBMs), achieving better performance in key space, correlation, sensitivity, and differential attack analyses for image encryption.
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.