Adaptive random-grid and progressive color secret sharing with CNN super-resolution on a many-core system
Document Type
Article
Publication Title
International Journal of Computers and Applications
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– (Formula presented.) for (Formula presented.) and (Formula presented.) for (Formula presented.) 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.
First Page
830
Last Page
839
DOI
10.1080/1206212X.2024.2389477
Publication Date
1-1-2024
Recommended Citation
Raviraja Holla, M. and Suma, D., "Adaptive random-grid and progressive color secret sharing with CNN super-resolution on a many-core system" (2024). Open Access archive. 10798.
https://impressions.manipal.edu/open-access-archive/10798
