DCM-Net: A Diffusion Model-Based Detection Network Integrating the Characteristics of Copy-Move Forgery

Abstract

Essentially, directly introducing any object detection network to perform copy-move forgery detection (CMFD) inevitably leads to low detection accuracy. Therefore, DCM-Net, an object detection network dominated by diffusion model that incorporates the characteristics of copy-move forgery, is proposed in this paper for obviously enhancing CMFD performance. DCM-Net, as the first diffusion model-based CMFD network, has the following three improvements. Firstly, the high-similarity box padding strategy pads high-similarity boxes, rather than random boxes used in diffusion model, to ground truth boxes, better guiding subsequent dual-attention detection heads (DDHs) to focus more on high-similarity regions. Secondly, different from previous deep learning based CMFD networks that utilize self-correlation calculation to indiscriminately transform all classification features extracted from feature extraction into high-similarly features, an adaptive feature combination strategy is proposed to obtain the optimal feature transformation capable of achieving the best detection performance, enabling DDHs to more effectively distinguish source and target regions. Finally, to make detection heads have more accurate source/target localization and distinguishment, DDHs equipped with efficient multi-scale attention and contextual transformer, are proposed to generate tampered features fusing the entire precise spatial position information and rich contextual global information. The experimental results carried out on three publicly available datasets including USC-ISI, CoMoFoD, and COVERAGE, demonstrate that DCM-Net outperforms several advanced algorithms in terms of similarity detection ability and source/target differentiation ability.