Visual image perception preservation through a compression-encryption framework

Abstract

Wireless Sensor Networks (WSNs) are increasingly deployed for monitoring both one-dimensional (1D) and two-dimensional (2D) environmental phenomena, generating vast amounts of sensitive data, often in the form of images, which are transmitted daily. Ensuring secure data transmission over untrusted communication channels is a persistent and critical challenge. Compressive Sensing (CS) has emerged as a powerful signal processing technique that enables simultaneous sampling and compression of signals. Secure Compressive Sensing (Sec-CS) has gained significant attention in information security, as it can serve as an integrated cryptographic mechanism that performs the functions of sampling, compression, and encryption while safeguarding the pseudo-random measurement matrix as a secret key. This paper presents a privacy-preserving, computationally efficient key-agreement framework for secure image exchange in WSN-based monitoring systems. The proposed architecture incorporates DNA encoding, chaotic mapping, and a lightweight XOR-based image encryption operation, all driven by a pseudo-random key vector. The framework not only achieves high computational efficiency but also demonstrates robustness against a range of cryptographic attacks. Extensive numerical simulations validate the proposed framework effectiveness, demonstrating its superiority over existing approaches in terms of both security and computational performance. A comprehensive security analysis further confirms that the proposed framework meets key security requirements, offering strong protection against diverse attack scenarios.