# Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering

arXiv: https://arxiv.org/abs/2102.01439 · HTML: https://mlsanity.com/paper/2102.01439/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1708.00930/) (2017)
- [Exposing Fake Images with Forensic Similarity Graphs](https://mlsanity.com/paper/1912.02861/) (2019)
- [A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection](https://mlsanity.com/paper/1909.06751/) (2019)
- [Detection and Localization of Image Forgeries using Resampling Features and Deep Learning](https://mlsanity.com/paper/1707.00433/) (2017)
- [Copy Move Source-Target Disambiguation through Multi-Branch CNNs](https://mlsanity.com/paper/1912.12640/) (2019)
- [Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics](https://mlsanity.com/paper/1904.02520/) (2019)
- [Primary quantization matrix estimation of double compressed JPEG images via CNN](https://mlsanity.com/paper/1908.04259/) (2019)

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

- [Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering](https://mlsanity.com/paper/2308.09307/) (2023)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization](https://mlsanity.com/paper/2408.02306/) (2024)
- [UVL2: A Unified Framework for Video Tampering Localization](https://mlsanity.com/paper/2309.16126/) (2023)
- [TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization](https://mlsanity.com/paper/2212.12841/) (2022)

## Abstract (arXiv)

Detection of inconsistencies of double JPEG artefacts across different image regions is often used to detect local image manipulations, like image splicing, and to localize them. In this paper, we move one step further, proposing an end-to-end system that, in addition to detecting and localizing spliced regions, can also distinguish regions coming from different donor images. We assume that both the spliced regions and the background image have undergone a double JPEG compression, and use a local estimate of the primary quantization matrix to distinguish between spliced regions taken from different sources. To do so, we cluster the image blocks according to the estimated primary quantization matrix and refine the result by means of morphological reconstruction. The proposed method can work in a wide variety of settings including aligned and non-aligned double JPEG compression, and regardless of whether the second compression is stronger or weaker than the first one. We validated the proposed approach by means of extensive experiments showing its superior performance with respect to baseline methods working in similar conditions.