Multi-Level Secure Image Cryptosystem Using Logistic Map Chaos: Entropy, Correlation, and 3D Histogram Validation

Abstrak

This study presents a multi-level image encryption framework that combines password dependent SHA-256 key generation with a Logistic Map-based chaotic mechanism, supporting three operational modes: Speed, Balanced, and Security. The system is designed for scalability and robustness across diverse image sizes, achieving up to 27 percent faster encryption than AES on 1024×1024 images while maintaining high cryptographic strength. Experimental results show strong randomness with entropy reaching up to 7.98 bits per pixel, reduced adjacent pixel correlation below 0.01, and high resistance to differential attacks with NPCR above 99.6 percent and UACI around 33.4 percent. Structural integrity after decryption is also preserved with SSIM scores above 0.98. Compared to existing chaos based methods such as those proposed by Arif et al. and Riaz et al., the proposed system offers superior entropy performance, enhanced flexibility through multi-mode encryption, and broader resolution support up to 2048×2048 pixels. Comprehensive evaluations using entropy, correlation, PSNR, SSIM, XOR, and 3D histogram analysis confirm the method’s effectiveness. These findings highlight the system’s suitability for real-time, secure image transmission in environments such as IoT, medical imaging, and embedded applications.