Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909159187808256 |
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| author | Wang, Jingyu Jing, Niantai Liu, Ziyao Nie, Jie Qi, Yuxin Chi, Chi-Hung Lam, Kwok-Yan |
| author_facet | Wang, Jingyu Jing, Niantai Liu, Ziyao Nie, Jie Qi, Yuxin Chi, Chi-Hung Lam, Kwok-Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00611 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining Wang, Jingyu Jing, Niantai Liu, Ziyao Nie, Jie Qi, Yuxin Chi, Chi-Hung Lam, Kwok-Yan Computer Vision and Pattern Recognition 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. |
| title | Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.00611 |