Secondary Labeling: A Novel Labeling Strategy for Image Manipulation Detection

Image manipulation detection methods typically rely on a binary annotation called Primary Labeling (PrLa) to identify tampered and authentic regions in a tampered image. However, PrLa only focuses on the difference between authentic and tampered regions, ignoring the distinctions among tampered regions in different images. This transforms the task of image manipulation detection into salient object detection, with the goal shifting towards identifying the most attention-grabbing objects in images. To address this issue, this paper proposes a novel labeling strategy called Secondary Labeling (SeLa). SeLa generates a query table containing multiple tampered categories and randomly reassigns these tampered classes to different types of tampered data, effectively improving the detection performance of models by refocusing the differences among the various data. Additionally, to further improve the detection performance, this paper introduces an Adaptive Label Smoothing (ALS) regularization method. This method addresses the loss of correlation among tampered classes in SeLa caused by the one-hot encoding method. Experimental results show that compared with PrLa, SeLa not only improves the performance of detection models by up to 17%, but also enhances the robustness and convergence rate.