A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

Shafique, A., Mehmood, A., Alawida, M., Elhadef, M. and Ur Rehman, M. (2024) A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks. Multimedia Tools and Applications, (doi: 10.1007/s11042-024-18959-6) (Early Online Publication)

Abstract

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in real-time applications.

• Item Type: Articles
• Additional Information: Funding statement Abu Dhabi University’s Office of Sponsored Programs in the United Emirates (Grant Number: 19300752) funded this endeavor.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shafique, Dr Arslan
• Authors: Shafique, A., Mehmood, A., Alawida, M., Elhadef, M., and Ur Rehman, M.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: Multimedia Tools and Applications
• Publisher: Springer
• ISSN: 1380-7501
• ISSN (Online): 1573-7721
• Published Online: 04 April 2024
• Copyright Holders: Copyright: © The Author(s) 2024
• First Published: First published in Multimedia Tools and Applications 2024
• Publisher Policy: Reproduced under a Creative Commons licence