ChaosFusion: An Adaptive 5D Hyper-chaotic Framework for Secure Image Encryption with 3D Pixel Permutation

Teja N. R. — 2026-09-11

The growing exchange of digital images across public networks has made image encryption a critical area of research. Conventional cryptosystems such as DES and AES are ill-suited to image data because of its large volume, high redundancy, and strong inter-pixel correlation, motivating the shift towards chaos-based encryption. This paper surveys recent literature on chaotic and hyper-chaotic image encryption techniques, examining pixel reorganization strategies, nonlinear transformations, dynamic S-box design, and hybrid approaches that combine chaos with logic gates or deep-learning autoencoders. Each approach is analyzed for its contribution to confusion–diffusion strength, keyspace, and computational cost. The survey identifies a recurring trade-off between security strength and processing efficiency across existing methods and uses these gaps to motivate a proposed framework based on 3D pixel scrambling and a 5D hyper-chaotic keystream. The survey further considers how adaptive confusion–diffusion can strengthen a practical encryption pipeline by allowing the chaotic keystream and permutation behavior to respond to secret parameters rather than relying on a fixed transformation sequence. Particular attention is given to the relationship between spatial scrambling, hyper-chaotic sequence generation, key sensitivity, and measurable security indicators. The reviewed studies show that increasing algorithmic complexity can improve resistance to statistical and differential analysis, but may also increase execution time, memory consumption, and implementation difficulty. Therefore, the proposed direction emphasizes a balanced architecture in which 3D pixel scrambling reduces spatial redundancy and a 5D hyper-chaotic keystream performs adaptive diffusion.

Explainable Neuro-fuzzy System for Coral Reef Fish Classification Using Multi-Scale Visual Features

Dontula Kavya Latha Harshini — 2026-09-18

Automated coral reef fish classification from underwater imagery is essential for scalable biodiversity monitoring, yet the deep convolutional networks achieving the highest accuracy remain largely opaque, limiting adoption by marine scientists who must justify conservation decisions. This article presents an explainable neuro-fuzzy system that classifies coral reef fish species from multi-scale visual features while producing human-readable justifications for every prediction. The proposed architecture extracts fine-, mid-, and coarse-scale convolutional features corresponding to local texture, body shape, and global coloration, fuses them into a unified embedding, and passes this embedding through a Takagi-Sugeno neuro-fuzzy inference layer whose linguistic rules and membership functions remain directly inspectable. A complementary explanation module combines fuzzy rule firing strengths with gradient-based saliency maps to generate joint symbolic-visual explanations. The architecture, its multi-scale feature extraction strategy, the fuzzy rule base, and representative underwater fish and coral datasets are described, and comparative tables summarize architectural components, datasets, and accuracy-interpretability trade-offs against convolutional and standard neuro-fuzzy baselines. Results indicate the proposed system retains competitive classification accuracy while substantially improving interpretability relative to conventional deep learning classifiers, supporting its use as a trustworthy tool for automated reef fish monitoring and conservation decision support.

Journal of Soft Computing and Computational Intelligence (p-ISSN: 3107-4855, e-ISSN: 3048-6610)

ChaosFusion: An Adaptive 5D Hyper-chaotic Framework for Secure Image Encryption with 3D Pixel Permutation

Explainable Neuro-fuzzy System for Coral Reef Fish Classification Using Multi-Scale Visual Features