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

• URL: http://arxiv.org/abs/2404.00611v2
• Date: Wed, 3 Apr 2024 07:18:11 GMT
• Title: Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
• Authors: Jingyu Wang, Niantai Jing, Ziyao Liu, Jie Nie, Yuxin Qi, Chi-Hung Chi, Kwok-Yan Lam,
• Abstract summary: 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. We operate experiments on three public datasets which validate the effectiveness and the robustness of the proposed IMNet.
• Score: 25.174869954072648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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