{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:18:07Z","timestamp":1759335487125},"reference-count":22,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2013,2,26]],"date-time":"2013-02-26T00:00:00Z","timestamp":1361836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security Comm Networks"],"published-print":{"date-parts":[[2015,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The wide use of high\u2010performance image acquisition devices and powerful image\u2010processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image\u2010splicing detection is of great importance in digital forensics. In this paper, an effective framework for revealing image\u2010splicing forgery is proposed. First, the local binary pattern operator is used to model magnitude components of two\u2010dimensional arrays obtained by applying multisize block discrete cosine transform to test images. Then, all of bins of histograms computed from local binary pattern codes are served as discriminative features for image\u2010splicing detection. After that, kernel principal component analysis is utilized to reduce the dimensionality of the proposed features to avoid the high computational complexity, high mutual correlation among the constructed features and possible overfitting for support vector machine classifier. Finally, support vector machine classifier is employed to distinguish spliced images from authentic images by using the final dimensionality\u2010reduced feature set. The experiment results show that the proposed method can perform better than some state\u2010of\u2010the\u2010art methods in terms of the detection performance over the Columbia image\u2010splicing detection evaluation dataset. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sec.721","type":"journal-article","created":{"date-parts":[[2013,2,26]],"date-time":"2013-02-26T08:58:45Z","timestamp":1361869125000},"page":"2386-2395","source":"Crossref","is-referenced-by-count":22,"title":["Image\u2010splicing forgery detection based on local binary patterns of DCT coefficients"],"prefix":"10.1002","volume":"8","author":[{"given":"Yujin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering Shanghai Jiao Tong University  Shanghai China"}]},{"given":"Chenglin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering Beijing University of Posts and Telecommunications  Beijing China"}]},{"given":"Yiming","family":"Pi","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering University of Electronic Science and Technology of China  Chengdu China"}]},{"given":"Shenghong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Shanghai Jiao Tong University  Shanghai China"}]},{"given":"Shilin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Security Engineering Shanghai Jiao Tong University  Shanghai China"}]}],"member":"311","published-online":{"date-parts":[[2013,2,26]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2046223"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2011.2164576"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2010.0400"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2064327"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2008.931079"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.03.019"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2010.2051426"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2008.2012215"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2010.2072921"},{"key":"e_1_2_7_11_1","unstructured":"NgTT ChangSF SunQ.Blind detection of photomontage using higher order statistics. 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