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arXiv · 2404.00611

Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining

Jingyu Wang·Niantai Jing·Ziyao Liu·Jie Nie·Yuxin Qi·Chi-Hung Chi·Kwok-Yan Lam
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Abstract

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.

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Jingyu Wang, Niantai Jing, Ziyao Liu, Jie Nie, Yuxin Qi, Chi-Hung Chi, Kwok-Yan Lam. 2024-03-31. Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining. https://arxiv.org/abs/2404.00611

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