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Image splicing is a very common image manipulation technique that issometimes used for malicious purposes. A splicing detec- tion and localizationalgorithm usually takes an input image and produces a binary decisionindicating whether the input image has been manipulated, and also asegmentation mask that corre- sponds to the spliced region. Most existingsplicing detection and localization pipelines suffer from two mainshortcomings: 1) they use handcrafted features that are not robust againstsubsequent processing (e.g., compression), and 2) each stage of the pipeline isusually optimized independently. In this paper we extend the formulation of theunderlying splicing problem to consider two input images, a query image and apotential donor image. Here the task is to estimate the probability that thedonor image has been used to splice the query image, and obtain the splicingmasks for both the query and donor images. We introduce a novel deepconvolutional neural network architecture, called Deep Matching and ValidationNetwork (DMVN), which simultaneously localizes and detects image splicing. Theproposed approach does not depend on handcrafted features and uses raw inputimages to create deep learned representations. Furthermore, the DMVN isend-to-end op- timized to produce the probability estimates and thesegmentation masks. Our extensive experiments demonstrate that this approachoutperforms state-of-the-art splicing detection methods by a large margin interms of both AUC score and speed.

Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection