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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>Recently, as one of the most popular covert communication technologies, generative steganography has received ever-increasing attention due to its promising performance against sophisticated steganalysis tools. However, it is quite difficult for the existing generative steganographic approaches to find a good tradeoff between hiding capacity and extraction accuracy, mainly due to the small capacity of their hiding spaces. To overcome this shortcoming, a Progressive Generative Steganography (PGS) network architecture is proposed to hide a secret message during the progressive image generation process to realize secure covert communication. Specifically, we first propose a robust Secret-to-Noise (S2N) mapping method to encode the secret message as a set of noise maps. Then, guided by these noise maps, a set of corresponding images ranging from low resolution to high resolution are progressively generated by the Single Generative Adversarial Networks (SINGAN). Consequently, a large-sized secret message can be hidden in the finally generated high-resolution image, since a set of high-capacity hiding spaces can be provided by the process of progressive image generation. Moreover, to improve the quality of image generation and the accuracy of secret message extraction, a Dense Secret-Feature Connection (DSFC) strategy is designed and integrated into the proposed PGS network architecture. Extensive experiments demonstrate that the proposed PGS outperforms the existing approaches in the aspects of both hiding capacity and message extraction, while maintaining promising anti-detectability and imperceptibility for covert communication.<\/jats:p>","DOI":"10.1145\/3760531","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T16:00:46Z","timestamp":1755532846000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Progressive Generative Steganography via High-Resolution Image Generation for Covert Communication"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-7169","authenticated-orcid":false,"given":"Zhili","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Guangdong Key Laboratory of Blockchain Security, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6575-6884","authenticated-orcid":false,"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-3658","authenticated-orcid":false,"given":"Zhengdao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-5668","authenticated-orcid":false,"given":"Huilin","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Oceanography, Jiangsu University of Science and Technology, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8523-7431","authenticated-orcid":false,"given":"Bin","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Guilin University of Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4114-4209","authenticated-orcid":false,"given":"Fengjun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Zhejiang Informatization Development Institute, Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3825-2230","authenticated-orcid":false,"given":"Yongfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"issue":"2","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TIT.2023.3340147","article-title":"Age-dependent differential privacy","volume":"70","author":"Zhang M.","year":"2023","unstructured":"M. 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