MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods are promising when training and test data are well aligned, but perform poorly on independent tests. Moreover, due to the absence of authentic test images, their image-level detection specificity is in doubt. The key question is how to design and train a deep neural network capable of learning generalizable features sensitive to manipulations in novel data, whilst specific to prevent false alarms…

• NN
  National Natural Science Foundation of China

  Awards: U1703261, 62172420
• RU
  Renmin University of China
• FR
  Fundamental Research Funds for the Central Universities