  Image splicing is a very common image manipulation technique that is
sometimes used for malicious purposes. A splicing detec- tion and localization
algorithm usually takes an input image and produces a binary decision
indicating whether the input image has been manipulated, and also a
segmentation mask that corre- sponds to the spliced region. Most existing
splicing detection and localization pipelines suffer from two main
shortcomings: 1) they use handcrafted features that are not robust against
subsequent processing (e.g., compression), and 2) each stage of the pipeline is
usually optimized independently. In this paper we extend the formulation of the
underlying splicing problem to consider two input images, a query image and a
potential donor image. Here the task is to estimate the probability that the
donor image has been used to splice the query image, and obtain the splicing
masks for both the query and donor images. We introduce a novel deep
convolutional neural network architecture, called Deep Matching and Validation
Network (DMVN), which simultaneously localizes and detects image splicing. The
proposed approach does not depend on handcrafted features and uses raw input
images to create deep learned representations. Furthermore, the DMVN is
end-to-end op- timized to produce the probability estimates and the
segmentation masks. Our extensive experiments demonstrate that this approach
outperforms state-of-the-art splicing detection methods by a large margin in
terms of both AUC score and speed.
