Implementing Intelligent Encryption Using Machine Learning for Digital Information Real-Time Images

Abstract

Improving people's secrecy and data security is the goal of investigating the real-world use of machine learning for intelligent encryption of real-time picture text digital information in the big data domain. In addition, a preprocessing module to an existing convolutional neural network, AlexNet can be built, which helps with the unruly of actual image text data leakage and makes it easier to encrypt this data. A one-dimensional chaotic system called logistic-sine and a multi-dimensional chaotic system called Lorenz generate chaotic sequences that encode the image text. This strikes a balance between security and system responsiveness. This method builds a model for real-time picture text encryption by combining AlexNet with chaotic functions. After that, the model's performance is assessed through a simulation experiment.