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

Introduction

Artificial intelligence's (AI) meteoric rise in the big data age has altered people's daily routines and careers in profound ways. People are moving away from reading books on paper and towards more complex forms of real-time multimedia text understanding. On the other hand, real-time image text's pervasiveness on the internet poses a double-edged sword: on the one hand, it offers valuable information, but on the other, it puts individuals at risk of privacy breaches, which can affect both their professional and personal lives. Y. Ding, G. Wu, D. Chen and et.al., frequency with which intermediaries contact homeowners and the prevalence of fraud resulting from privacy breaches are both concerning. As a result, researchers across disciplines are increasingly concerned about the need to encrypt user data. The goal of this data security focus is to reduce the likelihood of privacy breaches and stop third parties from getting user data without their consent.

Customers can choose to encrypt picture text before sending it to cloud storage to make sure their personal information is safe. W. Sirichotedumrong and Y. Kinoshita, when users need to do common operations in the plaintext domain, like searching for specific files, after uploading image text to the cloud server, this approach becomes problematic. One easy way to decrypt files stored in the cloud is to download them to a local device, decrypt them, and then search through them in plaintext. S. Rezaei and X. Liu, Nevertheless, this approach imposes a heavy computational burden from large-scale data encryption and decryption operations and results in unnecessary storage and network costs owing to the abundance of redundant data. The approach's viability is further reduced by constraints like network bandwidth.

Therefore, when it comes to real-time image text, intelligent digital information encryption is very important. Z. Chen, X. Li and T.T. Phuong, a crucial component of AI, machine learning is indispensable in many fields, such as machine translation, voice recognition, picture segmentation, and NLP. In particular, the convolutional neural network (CNN) is well-known for its weight-sharing capabilities and local connectivity, making it a very effective model of feedforward neural networks. To enable autonomous learning of multi-level features from original data, such as image text, CNN arranges neurons in a way that detects overlapping features in visual fields [8]. This method offers great practical value in improving intelligent digital information encryption for real-time image text by addressing the issue of large computational and storage space losses caused by traditional methods of encrypting digital information in image text.

Encrypting real-time images is becoming more important due to the increasing need to safeguard individuals' privacy and security in this rapidly evolving information age. Complex innovations are introduced in this study. In order to encrypt digital information within real-time image text, CNN must first be transformed into AlexNet. A preprocessing module is integrated to achieve this. Secondly, the Lorenz and Logistic-Sine systems are two examples of one-dimensional chaotic systems that can be used to generate chaotic sequences for picture text encryption. This method assures both the effectiveness of the security measures and the system's real-time performance. Third, we'll use AlexNet and chaotic functions to build and test an encryption model for images and text in real time. In subsequent stages, this research will serve as an experimental benchmark to improve the privacy encryption effectiveness of picture text.