A robust image encryption method based on block diagonal chaotic architecture with CNN performance evaluation metric

期刊论文
发表日期：

2025年3月26日

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Vida Mamandi,
Nahid Ardalani,
Behbod Ghalamkari

摘要

In this paper, a new block diagonal chaotic model (BDC) is investigated due to higher necessity of advanced secure data transmission method in wireless medium and considerable limitation on computational storage space. In this model, the image sparse coding using BDC matrix in noisy and Rayleigh fading channels is studied. The structured BDC model is designed with two schemes, including chaotic pseudo inverse matrix (CPI-m) and block diagonal chaotic structured matrix (BDC-m) using two chaotic maps, in which the initial values for generating nonlinear chaotic sequences are used as private key at the receiver side. The simulation results show that the retrieved data measured based on CPI and BDC schemes outperforms the chaotic compressive sensing (CCS) method over AWGN and Rayleigh fading channels by 6% and 22.7%, respectively. Also, the sensitivity of recovered BDC-based image increases to 10⁽⁻³²⁾ compared to that of 10⁽⁻¹⁶⁾ for conventional CCS based image. Moreover, the encryption key space increases from 10⁽³⁷⁾ for standard CCS-based measurements to $\beta \times {10}^{111}$ $β \times 10^{111}$ for measured data based on the BDC model, which guarantees that the competitors access no information. Considering datasets from iterative reconstruction process, the image features are extracted using convolutional neural network (CNN) model by ResNet-18 and GoogleNet architectures. The improved performance of data based on CPI and BDC model is validated by retrieved images accuracy over the classifier support vector machine (SVM).