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MI-DCC: A Content-Aware Dynamic Cipher Composition Framework for Adaptive Multi-Image Encryption
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DOI：10.3390/technologies14080461.png)
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Abstract
En 中文
This paper presents MI-DCC (Multi-Image Dynamic Cipher Composition), a content-aware framework for secure multi-image encryption. Unlike conventional schemes applying uniform cryptographic processes, MI-DCC combines structured cross-image region permutation with adaptive dynamic cipher selection. The framework establishes deterministic inter-image dependencies through key-driven region exchange, then analyzes regional statistical complexity using entropy, variance, intensity, and gradient descriptors. An adaptive Dynamic Cipher Composition mechanism assigns appropriate primitives from a heterogeneous cryptographic library according to computed complexity scores, improving diversity while maintaining competitive performance. A global diffusion stage propagates local modifications across the entire image batch, strengthening resistance against differential and statistical attacks. Experimental results on benchmark grayscale and color images demonstrate near-ideal entropy (7.9993 bits/pixel), high NPCR (99.612%), UACI (33.465%), and negligible pixel correlation. These results indicate that MI-DCC provides effective adaptive behavior, strong statistical diffusion, and exact reversibility under the evaluated experimental configuration.
Keywords:
multi-image encryption
Dynamic Cipher Composition
cross-image diffusion
adaptive cipher selection
region-based encryption
content-aware cryptography
cryptographic diversity
image security
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Citations:
3.2K
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