# Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue

arXiv: https://arxiv.org/abs/2012.08697 · HTML: https://mlsanity.com/paper/2012.08697/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Diffusion Facial Forgery Detection](https://mlsanity.com/paper/2401.15859/) (2024)
- [Toward Generalizable Forgery Detection and Reasoning](https://mlsanity.com/paper/2503.21210/) (2025)
- [ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.10238/) (2024)
- [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://mlsanity.com/paper/2302.13004/) (2023)
- [ForgeryTTT: Zero-Shot Image Manipulation Localization with Test-Time Training](https://mlsanity.com/paper/2410.04032/) (2024)
- [Fusion Transformer with Object Mask Guidance for Image Forgery Analysis](https://mlsanity.com/paper/2403.12229/) (2024)
- [Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis](https://mlsanity.com/paper/2207.13064/) (2022)
- [Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption](https://mlsanity.com/paper/2207.10402/) (2022)
- [Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning](https://mlsanity.com/paper/2404.17310/) (2024)
- [CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning](https://mlsanity.com/paper/2311.13263/) (2023)
- [Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining](https://mlsanity.com/paper/2404.00611/) (2024)
- [TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization](https://mlsanity.com/paper/2212.12841/) (2022)
- [Copy-Move Forgery Detection and Question Answering for Remote Sensing Image](https://mlsanity.com/paper/2412.02575/) (2024)
- [MSN: Multi-directional Similarity Network for Hand-crafted and Deep-synthesized Copy-Move Forgery Detection](https://mlsanity.com/paper/2512.07110/) (2025)

## Abstract (arXiv)

Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially for copy-move forgery detection. The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue. In the first stage, atrous convolution and skip matching are incorporated to enrich spatial information and leverage hierarchical features. Spatial attention is built on self-correlation to reinforce the ability to find appearance similar regions. In the second stage, Proposal SuperGlue is proposed to remove false-alarmed regions and remedy incomplete regions. Specifically, a proposal selection strategy is designed to enclose highly suspected regions based on proposal generation and backbone score maps. Then, pairwise matching is conducted among candidate proposals by deep learning based keypoint extraction and matching, i.e., SuperPoint and SuperGlue. Integrated score map generation and refinement methods are designed to integrate results of both stages and obtain optimized results. Our two-stage framework unifies end-to-end deep matching and keypoint matching by obtaining highly suspected proposals, and opens a new gate for deep learning research in copy-move forgery detection. Experiments on publicly available datasets demonstrate the effectiveness of our two-stage framework.