# Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

arXiv: https://arxiv.org/abs/2207.09135 · HTML: https://mlsanity.com/paper/2207.09135/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016)
- [A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization](https://mlsanity.com/paper/1703.04636/) (2017)

## Built on by (most cited first, 5 of 5)

- [Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization](https://mlsanity.com/paper/2408.02306/) (2024)
- [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)
- [An Effective Image Copy-Move Forgery Detection Using Entropy Information](https://mlsanity.com/paper/2312.11793/) (2023)
- [Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid Missed Detection and False Alarm](https://mlsanity.com/paper/2406.03271/) (2024)

## Abstract (arXiv)

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.