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First, a strategy of simulating steganography noise was adopted, and the distribution of steganography noise was learned by convolutional neural network, and a small amount of noise was added to the position of the secret message accurately to complete the attack on the steganography information, while protecting the image content to the maximum extent. In addition, different image recovery modules are designed in the deep network, such as the shallow feature extraction module, progressive attention recovery module, and detail feature reconstruction module, which collectively leverage hierarchical pixel features to mitigate discrepancies between reconstructed and original images while preserving visual fidelity before and after image attacks. Through two kinds of loss functions, the deep network model continuously optimizes the network performance to achieve the minimum degree of damage to the image content and the maximum degree of recovery of the reconstructed image. Experimental results show that the proposed method is superior to other methods in erasing secret message and restoring image quality.<\/jats:p>","DOI":"10.1155\/int\/9958912","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:14:14Z","timestamp":1758708854000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Steganography Defense Network Based on Simulation of Steganography Information Distribution"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1184-301X","authenticated-orcid":false,"given":"Jinjin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sa","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3455-569X","authenticated-orcid":false,"given":"Fengyuan","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8677-516X","authenticated-orcid":false,"given":"Yuanyuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2009.08.010"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2012.2190402"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12043"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2330"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.10.023"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-020-00518-2"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-020-00941-8"},{"key":"e_1_2_11_8_2","unstructured":"CorleyI. 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