Semantic Modeling and Pixel Discrimination for Image Manipulation Detection

Abstract

Image manipulation methods, such as the copy-move, splicing, and removal methods, have become increasingly mature and changed the common perception of “seeing is believing.” The credibility of digital media has been seriously damaged with the development of image manipulation methods. Most image manipulation detection methods detect traces of tampering pixel by pixel. As a result, the detected manipulation areas are separated, which results in insufficient consideration of content manipulation at the object level. In this paper, the detection of image manipulation areas based on forgery object detection and pixel discrimination is proposed. Specifically, the pixel-level detection branch resamples features and uses an LSTM to detect manipulations, such as resampling, rotation, and cropping. The goal of the forgery object detection branch, which is based on Faster R-CNN, is to extract the regions of interest and analyze the regions with high contrast as well as the forgery objects of the image. Furthermore, the fused heatmaps of the two branches are integrated with the object detection results. The noise in the heatmaps is shielded based on the forgery object information of the region proposal network. Experimental results on multiple standard forgery datasets have demonstrated the superiority of our proposed method compared with the state-of-the-art methods.