Deep Fusion Network for Splicing Forgery Localization

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


Digital splicing is a common type of image forgery: some regions of an image are replaced with contents from other images. To locate altered regions in a tampered picture is a challenging work because the difference is unknown between the altered regions and the original regions and it is thus necessary to search the large hypothesis space for a convincing result. In this paper, we proposed a novel deep fusion network to locate tampered area by tracing its border. A group of deep convolutional neural networks called Base-Net were firstly trained to response the certain type of splicing forgery respectively. Then, some layers of the Base-Net are selected and combined as a deep fusion neural network (Fusion-Net). After fine-tuning by a very small number of pictures, Fusion-Net is able to discern whether an image block is synthesized from different origins. Experiments on the benchmark datasets show that our method is effective in various situations and outperform state-of-the-art methods.


Image forensics Splicing forgery detection Forgery localization Deep convolutional network Fusion network 



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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MacauTaipaChina

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