# Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

arXiv: https://arxiv.org/abs/1802.03154 · HTML: https://mlsanity.com/paper/1802.03154/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization](https://mlsanity.com/paper/2108.04508/) (2021)
- [HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis](https://mlsanity.com/paper/2111.04703/) (2021)
- [SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery](https://mlsanity.com/paper/2108.12534/) (2021)

## Abstract (arXiv)

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms.