Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
<|reference_start|>Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining: 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.<|reference_end|>
@article{wang2024object-level,
    title={Object-level Copy-Move Forgery Image Detection based on Inconsistency
  Mining},
    author={Jingyu Wang, Niantai Jing, Ziyao Liu, Jie Nie, Yuxin Qi, Chi-Hung Chi,
  Kwok-Yan Lam},
    journal={arXiv preprint arXiv:2404.00611},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00611},
    primaryClass={cs.CV}
}