Adaptive random-grid and progressive color secret sharing with CNN super-resolution on a many-core system

Abstract

This study introduces an innovative method combining Convolutional Neural Networks (CNN) with random grid generation for enhanced visual secret sharing. Our approach supports color images, reduces processing time, and offers non-pixel expansion and flexible share combinations. Utilizing GPU computation, it significantly improves practicality and efficiency by operating in a feed-forward manner, avoiding complex optimization. Experimental results demonstrate superior metrics: maximum PSNR of 32 dB, 99% NCC correlation with benchmarks, 2.9% NAE, and SSIM of 98%. We achieve substantial speedups– 493 × for 256 × 256 and 3145 × for 1024 × 1024 images–compared to sequential models. The scheme exhibits robustness against attacks, evidenced by CMY component and share histogram similarities, and scalability shown in combinatorial explosion visualization. These findings underscore the efficacy and efficiency of our approach, advancing secure image sharing applications significantly.