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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2017,5,31]]},"abstract":"<jats:p>For a given image, it is a challenging task to generate its corresponding counterpart with visual inconspicuous modification. The complexity of this problem reasons from the high correlativity between the editing operations and vision perception. Essentially, a significant requirement that should be emphasized is how to make the object modifications hard to be found visually in the generative counterparts. In this article, we propose a novel dual-domain perceptual framework to generate visual inconspicuous counterparts, which applies the perceptual bidirectional similarity metric (PBSM) and appearance similarity metric (ASM) to create the dual-domain perception error minimization model. The candidate targets are yielded by the well-known PatchMatch model with the strokes-based interactions and selective object library. By the dual-perceptual evaluation index, all candidate targets are sorted to select out the best result. For demonstration, a series of objective and subjective measurements are used to evaluate the performance of our framework.<\/jats:p>","DOI":"10.1145\/3068427","type":"journal-article","created":{"date-parts":[[2017,4,28]],"date-time":"2017-04-28T12:38:23Z","timestamp":1493383103000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual-Domain Perceptual Framework for Generating Visual Inconspicuous Counterparts"],"prefix":"10.1145","volume":"13","author":[{"given":"Zhuo","family":"Su","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zeng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanhui","family":"Li","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaonan","family":"Luo","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,4,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2018396.2018421"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1576246.1531330"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2037676.2037678"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_30"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1661412.1618470"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1778820"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995344"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345401"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--8.","author":"Cho T.","key":"e_1_2_1_9_1","unstructured":"T. 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