Joint Quantization and Diffusion for Compressed Sensing Measurements of Natural Images

TL;DR

This paper introduces a joint quantization and diffusion method for compressed sensing measurements of natural images, enhancing security against known-plaintext attacks while maintaining comparable reconstruction quality.

Contribution

It proposes a novel joint quantization and diffusion approach tailored for real-valued CS measurements, improving security without sacrificing reconstruction performance.

Findings

Resists known-plaintext attacks

Maintains comparable image reconstruction quality

Enhances security through nonlinear cryptographic diffusion

Abstract

Recent research advances have revealed the computational secrecy of the compressed sensing (CS) paradigm. Perfect secrecy can also be achieved by normalizing the CS measurement vector. However, these findings are established on real measurements while digital devices can only store measurements at a finite precision. Based on the distribution of measurements of natural images sensed by structurally random ensemble, a joint quantization and diffusion approach is proposed for these real-valued measurements. In this way, a nonlinear cryptographic diffusion is intrinsically imposed on the CS process and the overall security level is thus enhanced. Security analyses show that the proposed scheme is able to resist known-plaintext attack while the original CS scheme without quantization cannot. Experimental results demonstrate that the reconstruction quality of our scheme is comparable to that of the original one.