# Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection

arXiv: https://arxiv.org/abs/1705.09765 · HTML: https://mlsanity.com/paper/1705.09765/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 25 of 25)

- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [Explicit Visual Prompting for Universal Foreground Segmentations](https://mlsanity.com/paper/2305.18476/) (2023)
- [Detection and Localization of Facial Expression Manipulations](https://mlsanity.com/paper/2103.08134/) (2021)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement](https://mlsanity.com/paper/2303.08438/) (2023)
- [BioFors: A Large Biomedical Image Forensics Dataset](https://mlsanity.com/paper/2108.12961/) (2021)
- [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://mlsanity.com/paper/2302.13004/) (2023)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization](https://mlsanity.com/paper/2203.07824/) (2022)
- [MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images](https://mlsanity.com/paper/2207.09107/) (2022)
- [Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations](https://mlsanity.com/paper/2410.16953/) (2024)
- [Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation](https://mlsanity.com/paper/2508.20987/) (2025)
- [Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos](https://mlsanity.com/paper/2112.08117/) (2021)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization](https://mlsanity.com/paper/2308.09357/) (2023)
- [Active Adversarial Noise Suppression for Image Forgery Localization](https://mlsanity.com/paper/2506.12871/) (2025)
- [Using Keypoint Matching and Interactive Self Attention Network to verify Retail POSMs](https://mlsanity.com/paper/2110.03646/) (2021)
- [A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks](https://mlsanity.com/paper/2112.00803/) (2021)
- [UGD-IML: A Unified Generative Diffusion-based Framework for Constrained and Unconstrained Image Manipulation Localization](https://mlsanity.com/paper/2508.06101/) (2025)

## Abstract (arXiv)

Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes. A splicing detec- tion and localization algorithm usually takes an input image and produces a binary decision indicating whether the input image has been manipulated, and also a segmentation mask that corre- sponds to the spliced region. Most existing splicing detection and localization pipelines suffer from two main shortcomings: 1) they use handcrafted features that are not robust against subsequent processing (e.g., compression), and 2) each stage of the pipeline is usually optimized independently. In this paper we extend the formulation of the underlying splicing problem to consider two input images, a query image and a potential donor image. Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images. We introduce a novel deep convolutional neural network architecture, called Deep Matching and Validation Network (DMVN), which simultaneously localizes and detects image splicing. The proposed approach does not depend on handcrafted features and uses raw input images to create deep learned representations. Furthermore, the DMVN is end-to-end op- timized to produce the probability estimates and the segmentation masks. Our extensive experiments demonstrate that this approach outperforms state-of-the-art splicing detection methods by a large margin in terms of both AUC score and speed.