MARATTO

article · Scientific Reports

Multiple image encryption algorithm using channel randomization and multiple chaotic maps

202421 citationsOpen accessSuez University

Abstract

Developing robust and secure image encryption methods for transmitting multiple images in batches over unprotected networks has become imperative. This necessity arises from the limitations of single-image encryption techniques in managing the escalating volume of extensive data. This paper introduces a novel three-layer multiple-image encryption (MIE) technique to encrypt batch images based on three 2D-chaotic maps. All multiple-color images are divided into RGB channels in the first layer. The images in each channel go through a randomization process to be arranged at random before being combined to form a single image (batch) used as the input for the following layer. In the second layer, chaotic sequences for scrambling pixels in each channel independently are generated using Baker, Henon, and 2-D Logistic chaotic maps, resulting in a scrambled image. The diffusion process is applied in the final layer by independently changing the values of the pixels in each channel using different chaotic sequences generated from the three maps and the XORing operation. The efficiency of the proposed scheme is validated through key sensitivity, key space analysis, complexity analysis, entropy assessment, and tests, including horizontal, vertical, and diagonal correlation, MSE, PSNR, UACI, and NPCR. Moreover, experimental results and a thorough security analysis affirm that the proposed encryption technique has effectively attained confidentiality and robust resistance against various attacks.

Research topics

  • Chaos-based Image/Signal Encryption
  • Advanced Steganography and Watermarking Techniques
  • Cryptographic Implementations and Security

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1038/s41598-024-79282-6

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.