{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:32:22Z","timestamp":1783539142689,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Current general image forgery localization (GIFL) methods confront two main challenges: decoder overconffdence causing misidentiffcation of the authentic regions or incomplete predicted masks, and limited accuracy in localizing forgery details. Recently, diffusion models have excelled as dominant approach for generative models, particularly effective in capturing complex scene details. However, their potential for GIFL remains underexplored. Therefore, we propose a GIFL framework named ForgDiffuser with diffusion models. The core of ForgDiffuser lies in leveraging diffusion models conditioned on the forgery image to efffciently generate the segmentation mask for tampered regions. Speciffcally, we introduce the attentionguided module (AGM) to aggregate and enhance image feature representations. Meanwhile, we design the boundary-driven module (BDM) with edge supervision to improve the localization accuracy of boundary details. Additionally, the probabilistic modeling and stochastic sampling mechanisms of diffusion models effectively alleviate the overconffdence issue commonly observed in traditional decoders. Experiments on six benchmark datasets demonstrate that ForgDiffuser outperforms existing mainstream GIFL methods in both localization accuracy and robustness, especially under challenging manipulation conditions.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/218","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1954-1962","source":"Crossref","is-referenced-by-count":2,"title":["ForgDiffuser: General Image Forgery Localization with Diffusion Models"],"prefix":"10.24963","author":[{"given":"Mengxi","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaozhang","family":"Niu","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, China"},{"name":"Southeast Digital Economy Development Institute, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, China"},{"name":"Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism (BUPT), China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:20Z","timestamp":1758627200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/218"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/218","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}