Q1 2026

An improved Bülban map and its application in multiple image encryption

Noura H. El Shabasy · Ahmed Mansy · Wassim Alexan
10.1007/s44443-026-00650-5 396 المشاهدات 3 الاقتباسات
3
الاقتباسات
396
المشاهدات
الملخص

Abstract

This article proposes a hybrid satellite image encryption algorithm integrating a modified 1D Bülban chaotic map, a Chinese Go-inspired permutation, and Deoxyribonucleic Acid-based encoding. The Bülban variant adds a sinusoidal perturbation to widen chaotic regions and heighten plaintext and key sensitivity for lightweight, high-entropy key-streams suited to on-board constraints, the Chinese Go permutation utilizes movement and capture logs to remap pixel coordinates in a manner dependent on both the key and plaintext. This effectively breaks the long-range spatial structures typical of satellite imagery, and Deoxyribonucleic Acid encoding supplies per-byte rule variability to strengthen diffusion with low computational cost. Security analysis demonstrates exceptional performance: near-ideal entropy (


$$\approx 7.99$$



7.99




), near-zero pixel correlation, and strong differential resistance with number of pixel change rate and unified average changing intensity values of


$$99.5927\%$$


99.5927
%




and


$$29.27038\%$$


29.27038
%




, respectively. The algorithm also achieves a massive key space of


$$2^{478}$$


2
478




, exhibits robustness to occlusion and noise attacks, and allows for lossless decryption (Structural Similarity Index Measure = 1). With an encryption time of 1.469 seconds for each of the images in a


$$256\times 256 \times 256$$


256
×
256
×
256




pixels image cube, the proposed algorithm balances high security with practical efficiency for satellite imaging pipelines.

الاستشهاد بهذا المقال (APA)
Noura, H. E. S., Ahmed, M., Wassim, A. (2026). An improved Bülban map and its application in multiple image encryption. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1007/s44443-026-00650-5
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نُشر في
الرقم الدولي ISSN 1319-1578
الربعية Q1
درجة المؤشر القياس العربي 100
التخصص Computer Science & AI
الناشر Elsevier / King Saud University
الدولة 🇸🇦 Saudi Arabia
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السنة 2026
اللغة English
أُضيف في 06 Jul 2026