Joint Quantization and Diffusion for Compressed Sensing Measurements of Natural Images
This work addresses security concerns for digital storage of CS measurements in image processing, though it is incremental as it builds on existing CS paradigms.
The paper tackles the security vulnerability of compressed sensing (CS) measurements in digital storage by proposing a joint quantization and diffusion approach for natural images, enhancing security against known-plaintext attacks while maintaining comparable reconstruction quality.
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.