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

arXiv: https://arxiv.org/abs/2404.00611 · HTML: https://mlsanity.com/paper/2404.00611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue](https://mlsanity.com/paper/2012.08697/) (2020)
- [Copy Move Source-Target Disambiguation through Multi-Branch CNNs](https://mlsanity.com/paper/1912.12640/) (2019)

## Built on by (most cited first, 1 of 1)

- [Copy-Move Forgery Detection and Question Answering for Remote Sensing Image](https://mlsanity.com/paper/2412.02575/) (2024)

## Abstract (arXiv)

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.