Recaptured Image Detection Based on Multi-Scale Residual Features of Discriminative Regions

The rapid development of digital cameras and smartphones makes it easy for people to record the information displayed in the media and obtain high-quality recaptured images, which would pose a serious threat to copyright protection, identity authentication, and public social security. Therefore, detecting recaptured images is an urgent problem in the multimedia forensics community. Most existing methods for detecting recaptured images focus on mining specific traces left in the images during the recapture operation. However, these traces may be covered up in certain environmental settings. In order to address this issue, we explore the internal differences in image statistics between the original and recaptured images, which do not depend on specific traces, and construct a more robust feature for detecting recaptured images. Firstly, the most discriminative regions are extracted based on the measure of pixel dispersion. Secondly, a multi-scale residual feature is constructed by calculating the first-order statistics of residual images to enhance the robustness against various recapture environments. Lastly, binary grey wolf optimization and particle swarm optimization (BGWOPSO) feature selection method is used to reduce dimensions in the features space, which could keep a good balance between performance and computational complexity. Experimental results on three public databases demonstrate that our proposed method significantly improves detection performance, especially on the most difficult-to-detect ICL-COMMSP database.