Dual-Region Encryption Model Based on a 3D-MNFC Chaotic System and Logistic Map

TL;DR

This paper introduces a new encryption model for portrait images that separately encrypts facial and non-facial regions to improve efficiency and security.

Contribution

A dual-region encryption model using a 3D-MNFC chaotic system and logistic map for efficient and secure facial image encryption.

Findings

The model achieves a large key space of 2536 and high information entropy of 7.9995.

NPCR and UACI values of 99.6055% and 33.4599% demonstrate strong encryption performance.

The model improves encryption efficiency by at least 37.82% compared to traditional methods.

Abstract

Facial information carries key personal privacy, and it is crucial to ensure its security through encryption. Traditional encryption for portrait images typically processes the entire image, despite the fact that most regions lack sensitive facial information. This approach is notably inefficient and imposes unnecessary computational burdens. To address this inefficiency while maintaining security, we propose a novel dual-region encryption model for portrait images. Firstly, a Multi-task Cascaded Convolutional Network (MTCNN) was adopted to efficiently segment facial images into two regions: facial and non-facial. Subsequently, given the high sensitivity of facial regions, a robust encryption scheme was designed by integrating a CNN-based key generator, the proposed three-dimensional Multi-module Nonlinear Feedback-coupled Chaotic System (3D-MNFC), DNA encoding, and bit reversal. The 3D-MNFC incorporating time-varying parameters, nonlinear terms and state feedback terms and coupling mechanisms has been proven to exhibit excellent chaotic performance. As for non-facial regions, the Logistic map combined with XOR operations is used to balance efficiency and basic security. Finally, the encrypted image is obtained by restoring the two ciphertext images to their original positions. Comprehensive security analyses confirm the exceptional performance of the regional model: large key space (2536) and near-ideal information entropy (7.9995), NPCR and UACI values of 99.6055% and 33.4599%. It is worth noting that the model has been verified to improve efficiency by at least 37.82%.

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