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To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) has been proposed and achieved success for spatial image steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its antidetectability and training efficiency should be improved. In conventional steganography, research has shown that the side information calculated from the precover can be used to enhance security. However, it is hard to calculate the side information without the spatial domain image. In this work, an embedding cost learning framework for JPEG image steganography via a GAN (JS\u2013GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side information (ESI). Experimental results have demonstrated that the proposed method can automatically learn a content\u2010adaptive embedding cost function, and using the ESI properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with a quality factor of 75 and 0.4 bpnzAC, the proposed JS\u2013GAN can increase the detection error by 2.58% over J\u2010UNIWARD, and the ESI\u2013aided version JS\u2013GAN (ESI) can further increase the security performance by 11.25% over JS\u2013GAN.<\/jats:p>","DOI":"10.1155\/int\/5309734","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:35:37Z","timestamp":1739752537000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["JPEG Image Steganography With Automatic Embedding Cost Learning"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-9230","authenticated-orcid":false,"given":"Jianhua","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4577-8501","authenticated-orcid":false,"given":"Yi","family":"Liao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4037-259X","authenticated-orcid":false,"given":"Fei","family":"Shang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3134-0353","authenticated-orcid":false,"given":"Xiangui","family":"Kang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7129-9153","authenticated-orcid":false,"given":"Yifang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yun-Qing","family":"Shi","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,2,16]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2011.2134094"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/1687-417X-2014-1"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2473815"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.109370"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3417268"},{"key":"e_1_2_11_6_2","doi-asserted-by":"crossref","unstructured":"SongX. 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