In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-level targets with intact structures. Focus on these challenges, this paper proposes an Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining (IMNet). To obtain complete object-level targets, we customize prototypes for both the source and tampered regions and dynamically update them. Additionally, we extract inconsistent regions between coarse similar regions obtained through self-correlation calculations and regions composed of prototypes. The detected inconsistent regions are used as supplements to coarse similar regions to refine pixel-level detection. We operate experiments on three public datasets which validate the effectiveness and the robustness of the proposed IMNet.
@article{arxiv.2404.00611,
title = {Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining},
author = {Jingyu Wang and Niantai Jing and Ziyao Liu and Jie Nie and Yuxin Qi and Chi-Hung Chi and Kwok-Yan Lam},
journal= {arXiv preprint arXiv:2404.00611},
year = {2024}
}