3D-BCNN-Based Image Encryption With Finite Computing Precision

Abstract

To resolve the finite computing precision problem in chaotic cryptography, and the security and efficiency drawbacks of deep learning-based image cryptosystems, an image encryption framework with low computing precision is presented based on three-dimensional Boolean convolution neural network (3D-BCNN). Unlike traditional CNN, the proposed 3D-BCNN is composed of only convolutional layers, in which a cross-channel 3-D Boolean convolution operation is devised without training and parameter optimization. To strengthen the security and sensitivity of the cryptosystem, the kernels and convolutional matrices are generated by the combined prime modulo multiplicative linear congruence generators, SHA-1, and a plain-image. All operations of 3D-BCNN can be conducted on devices with 8-bit word length, so the proposed encryption scheme could work well with low computing precision. Simulation results demonstrate that, with the largest computing accuracy 2−8, the proposed encryption scheme has high security and low time complexity, and can withstand various attacks.