Colorization-Driven Generative Secret Image Sharing

To enhance shares visual quality and security, meaningful secret image sharing relies on pre-input cover images to endow shadow images with interpretable semantics. However, the recently proposed schemes often yield shadows with mediocre visual quality and compromised security, such as vulnerability to statistical analysis or information leakage. Generative SIS (GSIS) introduces image generation or other operations, either to generate high-quality shadows or to eliminate the need for pre-input covers. Our prior \((2,2)\) -GSIS generated meaningful shares without covers but incurred non-critical leakage and did not support lossless reconstruction. Grayscale image colorization, being a widely adopted image processing operation, offers a promising route for GSIS by enriching semantics through chrominance synthesis. We introduce a colorization-driven GSIS. Chrominance components are shared via a \((k,n)\) -threshold SIS. Near-neutral chrominance from color templates provides structural priors that guide the synthesis of share pixels. The generated chrominance supersedes the template values and directly participates in colorization. This dynamic constraint departs from the linear modification paradigm of cover-based schemes, yielding shares that are visually natural and semantically preserved, without information leakage, and enabling lossless recovery from any \(k\) of \(n\) shares. Theoretical analysis and experiments validate the effectiveness and advantages of the framework.