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The method involves the following steps: first, affine transformation invariant MSERs are used to maintain the geometrical transformation invariance and reduce computational complexity; second, LIOP features are used to describe the texture and resist illumination change; and finally, RANdom SAmple Consensus is applied to remove false matches. The experiments indicate that the proposed method has great performance for scaling, rotation and illumination changes. Moreover, the method has the high robustness to Gaussian noise, Gaussian blur and JPEG compression.<\/jats:p>","DOI":"10.3233\/jifs-182647","type":"journal-article","created":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T11:48:23Z","timestamp":1574164103000},"page":"7761-7768","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Copy-move forgery detection based on local intensity order pattern and maximally stable extremal regions"],"prefix":"10.1177","volume":"37","author":[{"given":"Ye","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Intelligence Artificial, Hebei University of Technology, Tianjin, China"},{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"given":"Xuanjing","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, 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