A Dense U-Net with Cross-Layer Intersection for Detection and Localization of Image Forgery

In this paper, we apply cross-layer intersection mechanism to dense u-net for image forgery detection and localization. We first train DenseNet for binary classification. Spatial rich model (SRM) filters are adopted for capturing residual signals in the detected images. Then we propose a new approach to preserve complete feature maps of fully connected layer and consider them as the spatial decision information for image segmentation. In addition, these features in downsampling path are transferred more effectively and densely to upsampling path through multiscale upsampling and concatenation. A multi-stage training scheme is then applied to improve the convergence of the network. The experimental results show that the proposed method works well on several standard datasets.