{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:07:24Z","timestamp":1784214444351,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Flemish Government"},{"name":"IDLab (Ghent University\u2014imec)"},{"name":"Research Foundation\u2014Flanders (FWO)"},{"name":"Flanders Innovation &amp; Entrepreneurship (VLAIO)"},{"name":"European Union"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and localization methods to combat this issue. While some recent detection methods demonstrate good performance, there is still a significant gap to be closed to consistently and accurately detect image manipulations in the wild. This paper aims to enhance forgery detection and localization by combining existing detection methods that complement each other. First, we analyze these methods\u2019 complementarity, with an objective measurement of complementariness, and calculation of a target performance value using a theoretical oracle fusion. Then, we propose a novel fusion method that combines the existing methods\u2019 outputs. The proposed fusion method is trained using a Generative Adversarial Network architecture. Our experiments demonstrate improved detection and localization performance on a variety of datasets. Although our fusion method is hindered by a lack of generalization, this is a common problem in supervised learning, and hence a motivation for future work. In conclusion, this work deepens our understanding of forgery detection methods\u2019 complementariness and how to harmonize them. As such, we contribute to better protection against image manipulations and the battle against disinformation.<\/jats:p>","DOI":"10.3390\/jimaging10010004","type":"journal-article","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T23:00:12Z","timestamp":1703545212000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Harmonizing Image Forgery Detection &amp; Localization: Fusion of Complementary Approaches"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0660-3190","authenticated-orcid":false,"given":"Hannes","family":"Mareen","sequence":"first","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University\u2014imec, 9052 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5186-9886","authenticated-orcid":false,"given":"Louis","family":"De Neve","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University\u2014imec, 9052 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5313-4158","authenticated-orcid":false,"given":"Peter","family":"Lambert","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University\u2014imec, 9052 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9530-3466","authenticated-orcid":false,"given":"Glenn","family":"Van Wallendael","sequence":"additional","affiliation":[{"name":"Internet Technology and Data Science Lab (IDLab), Ghent University\u2014imec, 9052 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","article-title":"Media Forensics and DeepFakes: An overview","volume":"14","author":"Verdoliva","year":"2020","journal-title":"IEEE J. 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