{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T12:06:47Z","timestamp":1779797207549,"version":"3.53.1"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The widespread availability of image editing tools and generative AI has made image forgery more accessible and deceptive, demanding more advanced localization techniques. Existing CNN-based methods are limited by local receptive fields, struggling with long-range dependencies, while Transformers suffer from the quadratic complexity of self-attention, hindering practical deployment. Moreover, effectively utilizing multi-scale features remains challenging. To address these challenges, we propose a Multi-scale Wavelet-enhanced U-Mamba network (MWEU-Mamba). The proposed framework employs a Mamba-based state space model as the backbone to achieve global contextual modeling with linear complexity. A wavelet enhancement module is introduced to integrate spatial\u2013frequency representations, improving sensitivity to subtle manipulation traces across scales, while a channel attention mechanism further amplifies forgery-relevant feature responses. Extensive experiments on six public benchmark datasets (e.g., CASIA and Coverage) demonstrate that the proposed method achieves state-of-the-art performance on multiple datasets in terms of pixel-level F1-score.<\/jats:p>","DOI":"10.3390\/info17060526","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:02:51Z","timestamp":1779793371000},"page":"526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale Wavelet-Enhanced U-Mamba Network for Image Forgery Localization"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6568-5155","authenticated-orcid":false,"given":"Bing","family":"Qi","sequence":"first","affiliation":[{"name":"College of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China"},{"name":"Department of Public Safety Technology, Hainan Vocational College of Political Science and Law, Haikou 571100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2177-8255","authenticated-orcid":false,"given":"Chunyang","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8519-7271","authenticated-orcid":false,"given":"Yuliang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Public Safety Technology, Hainan Vocational College of Political Science and Law, Haikou 571100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, B., Qi, X., Lukasiewicz, T., and Torr, P.H. 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