Image Splicing Localization via Semi-global Network and Fully Connected Conditional Random Fields

  • Xiaodong Cun
  • Chi-Man PunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


We address the problem of image splicing localization: given an input image, localizing the spliced region which is cut from another image. We formulate this as a classification task but, critically, instead of classifying the spliced region by local patch, we leverage the features from whole image and local patch together to classify patch. We call this structure Semi-Global Network. Our approach exploits the observation that the spliced region should not only highly relate to local features (spliced edges), but also global features (semantic information, illumination, etc.) from the whole image. Furthermore, we first integrate Fully Connected Conditional Random Fields as post-processing technique in image splicing to improve the consistency between the input image and the output of the network. We show that our method outperforms other state-of-the-art methods in three popular datasets.


Image splicing localization Image forgery localization Multimedia security 



This work was supported in part by the Research Committee of the University of Macau under Grant MYRG2018-00035-FST, and the Science and Technology Development Fund of Macau SAR under Grant 041/2017/A1.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MacauTaipaMacau

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