Copy-Move Forgery Image Detection Based on Cross-Scale Modeling and Alternating Refinement

The detection of image tampering, specifically copy detection, is an important problem in many domains such as military, media, and public opinion outlets. Effective means to detect such tampering is crucial in controlling the dissemination of false information. However, a major challenge in achieving high detection accuracy lies in the variability of the scale of the copied targets. To tackle this problem, we introduce an all-encompassing methodology called Cross-Scale Modeling and Alternating Refinement (CANet) to detect the genuine source and tampered region at the pixel level. CANet consists of three modules: the Cross-Scale Similar Region Detection (CS) module, the Edge-Supervised Tamper Region Detection (ET) module, and the Alternating Refinement (AR) module. The CS module extracts coarse similar region features by cross-scale correlation modeling, which can alleviate the scale gap between the source and tampered region. The obtained coarse similar region feature is refined by the AR module, in which we introduce the source and the tampered region as the auxiliary information and employ a two-stage process that sequentially models their global feature representations. The tampered region used in the AR module is obtained from the ET module using edge supervision with a salient edge selection scheme, and the source region is generated by the implicit modeling. We conducted experiments on the USC-ISI, CASIA v2.0, CoMoFoD, and MICC-F220 datasets separately. Results show that our method outperforms the state-of-the-art.