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

Abstract

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.