{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:39:49Z","timestamp":1760060389598,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62401599"],"award-info":[{"award-number":["62401599"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Image protection in privacy-sensitive domains, such as healthcare and military, exposes critical limitations in existing secret image sharing (SIS) schemes, including cumbersome shadow management, coarse-grained access control, and an inefficient storage-speed trade-off, which limits SIS in practical scenarios. Thus, this paper proposes two SIS schemes to address the above issues: the hierarchical control sharing scheme with Gaussian blur (HCSS-GB) and the image bit expansion-based sharing scheme (IBESS). For scenarios with limited storage space, HCSS-GB employs Gaussian blur to generate gradient-blurred cover images and integrates a controllable sharing model to produce meaningful shadow images without pixel expansion based on Shamir\u2019s secret sharing. Furthermore, to accommodate real-time application scenarios, IBESS employs bit expansion to combine the high bits of generated shadow images with those of blurred carrier images, enhancing operational efficiency at the cost of increased storage overhead. Experimental results demonstrate that both schemes achieve lossless recovery (with PSNR of \u221e, MSE of 0, and SSIM of 1), validating their reliability. Specifically, HCSS-GB maintains a 1:1 storage ratio with the original image, making it highly suitable for storage-constrained environments; IBESS exhibits exceptional efficiency, with sharing time as low as 2.1 s under the (7,8) threshold, ideal for real-time tasks. Comparative analyses further show that using carrier images with high standard deviation contrast (C\u03c3) and Laplacian-based sharpness (SL) significantly enhances shadow distinguishability, strengthening the effectiveness of hierarchical access control. Both schemes provide valuable solutions for secure image sharing and efficient shadow management, with their validity and practicality confirmed by experimental data.<\/jats:p>","DOI":"10.3390\/e27090893","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:10:36Z","timestamp":1756080636000},"page":"893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HCSS-GB and IBESS: Secret Image Sharing Schemes with Enhanced Shadow Management and Visual-Gradient Access Control"],"prefix":"10.3390","volume":"27","author":[{"given":"Huanrong","family":"Pan","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8119-1539","authenticated-orcid":false,"given":"Wei","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3923-8218","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9778-7783","authenticated-orcid":false,"given":"Yongqiang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103024","DOI":"10.1016\/j.artmed.2024.103024","article-title":"Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective","volume":"159","author":"Kumar","year":"2025","journal-title":"Artif. 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